A simultaneous enhancement of accuracy and diversity of predictions remains a challenge in ambiguous medical image segmentation (AMIS) due to the inherent trade-offs. While truncated diffusion probabilistic models (TDPMs) hold strong potential with a paradigm optimization, existing TDPMs suffer from entangled accuracy and diversity of predictions with insufficient fidelity and plausibility. To address the aforementioned challenges, we propose Ambiguity-aware Truncated Flow Matching (ATFM), which introduces a novel inference paradigm and dedicated model components. Firstly, we propose Data-Hierarchical Inference, a redefinition of AMIS-specific inference paradigm, which enhances accuracy and diversity at data-distribution and data-sample level, respectively, for an effective disentanglement. Secondly, Gaussian Truncation Representation (GTR) is introduced to enhance both fidelity of predictions and reliability of truncation distribution, by explicitly modeling it as a Gaussian distribution at Ttrunc instead of using sampling-based approximations. Thirdly, Segmentation Flow Matching (SFM) is proposed to enhance the plausibility of diverse predictions by extending semantic-aware flow transformation in Flow Matching (FM). Comprehensive evaluations on LIDC and ISIC3 datasets demonstrate that ATFM outperforms SOTA methods and simultaneously achieves a more efficient inference. ATFM improves GED and HM-IoU by up to 12% and 7.3% compared to advanced methods.

Predicting molecular properties is vital for drug discovery, but experimental measurement is costly and limited by scarce labeled data. Self-supervised molecular pretraining can leverage large unlabeled datasets, reducing dependence on extensive annotations. However, most methods struggle to preserve domain-specific chemical knowledge, especially clinically relevant substructures such as motifs. Random masking and generic graph augmentations often degrade critical chemical information and harm interpretability. Many approaches also work at a single scale-either atom or motif-missing opportunities for cross-scale integration. We propose A2M-Mol, a multi-perspective molecular pretraining framework that combines atom-level and motif-level views through four parallel graph constructions. This design enables cross-view alignment and multiscale fusion, explicitly encoding chemical knowledge. A2M-Mol employs a suite of self-supervised tasks, including cross-view correspondence, atomic reconstruction, global topology modeling, and property constraint enforcement, all coordinated via tailored contrastive learning. Extensive experiments across benchmarks and backbone architectures show consistent improvements over state-of-the-art methods. Ablation studies confirm strong synergies among the tasks. A2M-Mol maintains robust predictive accuracy across data scales, demonstrating effectiveness for real-world molecular property prediction and potential to accelerate drug discovery.

In domain continual medical image segmentation, distillation-based methods mitigate catastrophic forgetting by continuously reviewing old knowledge. However, these approaches often exhibit biases towards both new and old knowledge simultaneously due to confounding factors, which can undermine segmentation performance. To address these biases, we propose the Causality-Adjusted Data Augmentation (CauAug) framework, introducing a novel causal intervention strategy called the Texture-Domain Adjustment Hybrid-Scheme (TDAHS) alongside two causality-targeted data augmentation approaches: the Cross Kernel Network (CKNet) and the Fourier Transformer Generator (FTGen). (1) TDAHS establishes a domain-continual causal model that accounts for two types of knowledge biases by identifying irrelevant local textures (L) and domain-specific features (D) as confounders. It introduces a hybrid causal intervention that combines traditional confounder elimination with a proposed replacement approach to better adapt to domain shifts, thereby promoting causal segmentation. (2) CKNet eliminates confounder L to reduce biases in new knowledge absorption. It decreases reliance on local textures in input images, forcing the model to focus on relevant anatomical structures and thus improving generalization. (3) FTGen causally intervenes on confounder D by selectively replacing it to alleviate biases that impact old knowledge retention. It restores domain-specific features in images, aiding in the comprehensive distillation of old knowledge. Our experiments show that CauAug significantly mitigates catastrophic forgetting and surpasses existing methods in various medical image segmentation tasks.

Knowledge distillation can mitigate catastrophic forgetting in domain continual segmentation by transferring knowledge from the older model to the newer model. However, existing distillation-based methods primarily emphasize knowledge retention while overlooking inherent defects in the older teacher models. As a result, these teacher-originated defects, such as knowledge gaps or biases, are propagated and exacerbate forgetting. To address this challenge, we propose a Targeted Knowledge Rectification Learning framework (TKRL) to probe and correct teacher-originated defects. TKRL consists of two modules: 1) Probe-augmented Class Distillation, which generates gradient-driven "probes" to uncover underrepresented features in the older model, thereby bridging knowledge gaps by distilling hidden information into the new model; 2) Variance-guided Masked Autoencoder, which selectively masks and reconstructs critical high-uncertainty patches across multi-level semantic regions, thereby correcting biases inherited from the older model. Our experimental results show that TKRL effectively rectifies knowledge gaps and biases, thereby mitigating catastrophic forgetting and enhancing performance in domain continual segmentation.

With the growing global threat of coronary artery disease (CAD), automated CAD diagnosis techniques based on coronary CT angiography (CCTA) have been developed. However, their clinical applicability remains limited due to the heterogeneity of stenosis and plaque attributes, as well as confounders within the causal relationships of CAD diagnosis. This work introduces the Attribute-Decoupled Intervention Network (ADI-Net), a confounder-free CAD diagnosis framework designed for fine-grained analysis at both the artery and patient levels, aligning with real-world clinical practice. ADI-Net employs an attribute-decoupled representation that effectively captures the heterogeneous features of stenosis and plaque with differential constraints, enabling precise, fine-grained classification. Additionally, the dynamic-updating causal intervention continuously refines confounder banks and applies the Do-expression within a complete causality, ensuring comprehensive, cross-level assessments. Experiments on CCTA datasets from three clinical centers demonstrate that ADI-Net outperforms state-of-the-art methods in cross-level, fine-grained CAD diagnosis, exhibiting superior robustness, domain adaptability, and data efficiency.

Coronary Artery Disease (CAD) poses a significant threat to cardiovascular patients worldwide, underscoring the critical importance of automated CAD diagnostic technologies in clinical practice. Previous technologies for lesion assessment in Coronary CT Angiography (CCTA) images have been insufficient in terms of interpretability, resulting in solutions that lack clinical reliability in both network architecture and prediction outcomes, even when diagnoses are accurate. To address the limitation of interpretability, we introduce the Trusted Lesion-Assessment Network (TLA-Net), which provides a clinically reliable solution for multi-view CAD diagnosis: (1) The causality-informed evidence collection constructs a causal graph for the diagnostic process and implements causal interventions, preventing confounders' interference and enhancing the transparency of the network architecture. (2) The clinically-aligned uncertainty integration hierarchically combines Dirichlet distributions from various views based on clinical priors, offering confidence coefficients for prediction outcomes that align with physicians' image analysis procedures. Experimental results on a dataset of 2,618 lesions demonstrate that TLA-Net, supported by its interpretable methodological design, exhibits superior performance with outstanding generalization, domain adaptability, and robustness.

The latest clinical guideline of the Coronary Artery Disease Reporting and Data System (CAD-RADS) emphasizes comprehensive CAD risk evaluation, driving the development of automated diagnosis technologies toward a unified multi-task framework. Previous task-specific architectures, relying on varied pre- and post-processing, showed redundancy and prediction inconsistencies. To address this, we first proposed synergistic multitask learning and constructed a unified CAD-RADS framework. It provides a collaborative diagnosis of the CAD-RADS level, coronary artery calcium, the segment involvement score, and abnormality modifiers based on the patient's CT Angiography (CTA) volume. On the one hand, we integrate offset features across multiple scales to learn distinct attention distributions for different tasks in the latent space, thereby meeting the representation requirements for task customization within a unified architecture. On the other hand, we employ ExpectationMaximization (EM)-driven iterative optimization to interactively learn a compact basis consensus among tasks, balancing them and promoting semantic complementarity. Experimental results based on CTA volumes from 1,068 patients demonstrate our framework outperforms state-of-the-art methods, advancing the clinical application of intelligent CAD-RADS.

Neoadjuvant chemoradiotherapy (nCRT) is the standard treatment for locally advanced rectal cancer (LARC). However, substantial inter-patient variability in response to nCRT poses a significant challenge for accurately predicting treatment outcomes based on preoperative data, thereby complicating clinical decision-making. With the rapid advancement of artificial intelligence technologies, there has been growing interest in leveraging AI for predictive modeling in cancer therapy. In this study, we propose a novel multi-modal prediction framework, FMPNet, which integrates preoperative magnetic resonance imaging (MRI) and whole-slide image (WSI) features to predict nCRT efficacy. Specifically, for WSI processing, we develop an efficient tumor cell segmentation strategy and incorporate a deep subspace clustering mechanism into the feature extraction pipeline to enhance the model's representational capacity. Comprehensive experiments demonstrate that FMPNet consistently outperforms ten other feature fusion-based prediction models on both internal test sets and external validation cohorts across multiple metrics, including accuracy, precision, recall, F1-score and ROC-AUC curves. These results not only confirm the superior performance of our model but also underscore its potential to support more accurate and personalized clinical decision-making for patients with LARC.

Automated quality assessment of cardiac ultrasound images is crucial for ensuring diagnostic accuracy and clinical decision-making reliability. Hospitals face significant challenges in efficiently screening ultrasound image quality, where manual expert review is time-consuming and subjective. However, dedicated methods for ultrasound image quality assessment remain scarce, with most adapted from natural image quality metrics that fail to capture clinically relevant diagnostic factors. In this paper, we propose a novel dual-domain framework that models both spatial anatomical features and frequency-domain spectral characteristics using specialized neural modules. Our approach incorporates cardiac-specific attention mechanisms and wavelet-based artifact detection to enable comprehensive, clinically aligned evaluation. Extensive experiments on a largescale clinical dataset demonstrate the superiority of our method, achieving 88.1 % overall accuracy, a macro-averaged F1-score of 88.1 %, and 100 % precision and recall in detecting diagnostically unacceptable images. The proposed framework is designed to meet clinical reliability standards, paving the way for safe integration into diagnostic workflows.

Metal artifacts severely degrade the quality of CT images. Existing learning-based metal artifact reduction (MAR) methods often miss multi-scale anatomy and fail to transfer from synthetic to clinical scans. We present CLEAR-Net, which injects clinical priors and adaptively aligns corrupted features via two modules: CEAB, a quantized multi-scale anatomy bank distilled from clean clinical CTs, and FLAG, a scale-wise gating mechanism that aligns features to CEAB priors across domains. This structure-aware design preserves organ boundaries and fine textures, boosting robustness and generalization. Extensive experiments demonstrate CLEAR-Net's superior performance. The source code will be made publicly available.

In image generation, Schrödinger Bridge (SB)-based methods theoretically enhance the efficiency and quality compared to the diffusion models by finding the least costly path between two distributions. However, they are computationally expensive and time-consuming when applied to complex image data. The reason is that they focus on fitting globally optimal paths in high-dimensional spaces, directly generating images as next step on the path using complex networks through self-supervised training, which typically results in a gap with the global optimum. Meanwhile, most diffusion models are in the same path subspace generated by weights fA(t) and fB(t), as they follow the paradigm (xt= fA(t)xImg+ fB(t)ϵ). To address the limitations of SB-based methods, this paper proposes for the first time to find local Diffusion Schrödinger Bridges (LDSB) in the diffusion path subspace, which strengthens the connection between the SB problem and diffusion models. Specifically, our method optimizes the diffusion paths using Kolmogorov-Arnold Network (KAN), which has the advantage of resistance to forgetting and continuous output. The experiment shows that our LDSB significantly improves the quality and efficiency of image generation using the same pretrained denoising network and the KAN for optimising is only less than 0.1MB. The FID metric is reduced by more than 15%, especially with a reduction of 48.50% when NFE of DDIM is 5 for the CelebA dataset. Code is available at https://github.com/PerceptionComputingLab/LDSB.

T-cell receptors (TCR), which are heterodimers of $\alpha $ and $\beta $ chains that recognize foreign antigens, are of great significance to current immunotherapy. Although artificial intelligence (AI) has explosively accelerated de novo protein design, the challenge of therapeutic TCR design has been overlooked by most researchers. Existing TCR engineering relies heavily on isolating antigen-specific TCRs from tumor tissues, which requires a large amount of labor resources and wet experimental verification. To mitigate this issue, we present TCRdesign, a pretrained generative protein language model (PLM) for the de novo design of artificial TCR $\beta $-chain complementarity-determining region 3 sequences conditioned on antigen-binding specificity (BS). In parallel, we develop a high-accuracy binding predictor (TCRBinder) that couples paired $\alpha $/$\beta $ chain information with antigen sequences to assess BS. Our in silico comparisons demonstrate that (i) TCRdesign surpasses state-of-the-art baselines in generating antigen-specific TCR sequences. The model leverages paired-chain coherence to refine amino-acid level interaction patterns. (ii) TCRdesign-generated TCR sequences exhibit better antigen binding capability to diverse oncogenic hotspots compared with natural counterparts. (iii) TCRdesign inherits the intrinsic properties of large PLMs, enabling effectively identify the determinant residues in TCR-antigen binding, which enhances its interpretability. These results highlight the significant capability of TCRdesign in understanding and generating TCR sequences with an antigen-specific interaction pattern, charting a versatile path toward AI-driven T-cell engineering for precision immunotherapy.

Accurate prediction of binding between human leukocyte antigen (HLA) class I molecules and antigenic peptide segments is a challenging task and a key bottleneck in personalized immunotherapy for cancer. Although existing prediction tools have demonstrated significant results using established datasets, most can only predict the binding affinity of antigenic peptides to HLA and do not enable the immunogenic interpretation of new antigenic epitopes. This limitation results from the training data for the computational models relying heavily on a large amount of peptide-HLA (pHLA) eluting ligand data, in which most of the candidate epitopes lack immunogenicity. Here, we propose an adaptive immunogenicity prediction model, named MHLAPre, which is trained on the large-scale MS-derived HLA I eluted ligandome (mostly presented by epitopes) that are immunogenic. Allele-specific and pan-allelic prediction models are also provided for endogenous peptide presentation. Using a meta-learning strategy, MHLAPre rapidly assessed HLA class I peptide affinities across the whole pHLA pairs and accurately identified tumor-associated endogenous antigens. During the process of adaptive immune response of T-cells, pHLA-specific binding in the antigen presentation is only a pre-task for CD8+ T-cell recognition. The key factor in activating the immune response is the interaction between pHLA complexes and T-cell receptors (TCRs). Therefore, we performed transfer learning on the pHLA model using the pHLA-TCR dataset. In pHLA binding task, MHLAPre demonstrated significant improvement in identifying neoepitope immunogenicity compared with five state-of-the-art models, proving its effectiveness and robustness. After transfer learning of the pHLA-TCR data, MHLAPre also exhibited relatively superior performance in revealing the mechanism of immunotherapy. MHLAPre is a powerful tool to identify neoepitopes that can interact with TCR and induce immune responses. We believe that the proposed method will greatly contribute to clinical immunotherapy, such as anti-tumor immunity, tumor-specific T-cell engineering, and personalized tumor vaccine.

Bias fields inevitably degrade MRI that seriously interferes the diagnosis of physicians for accurate analysis, and removing it is a crucial image analysis task. Generative models (such as GANs) are used for bias field correction, and outperform traditional methods, however are hindered by the high cost of data annotation and instability during training. Recently, the diffusion-based methods have excelled over GANs in many applications, and they are powerful in removing noise from images, while the bias field can be regarded as a smooth noise. However, it is a challenge to directly apply to 3D bias field correction due to sampling inefficiency, the heavy computational demand, and implicit correction process. We propose a Memory-Efficient Multivariate Gaussian Bias Diffusion Model (MeMGB-Diff) that is an explicit, sampling, and memory both efficient diffusion model for 3D bias field correction without using clinical labels. MeMGB-Diff extends the diffusion models to multivariate Gaussian and models the bias field as a multivariate Gaussian variable, allowing direct diffusion and removal of the 3D bias fields without Gaussian noise. For memory efficiency, MeMGB-Diff performs diffusion model in smaller readable image domain at the expense of a negligible accuracy loss, based on the strong correlation among adjacent voxels of bias field. We also propose a loss function to mainly learn the intensity trend, which mainly causes the inhomogeneity of MRI, and effectively increases the correction accuracy. For comprehensive performance comparison, we propose a synthetic method for generating more varied bias fields during testing. Both quantitative and qualitative assessments on synthetic and clinical data confirm the high fidelity and uniform intensity of our results. MeMGB-Diff reduces data size by 64 times to use less memory, improves sampling efficiency by more than 10 times compared to other diffusion-based methods, and achieves optimal metrics, including SSIM, PSNR, COCO, and CV for various tissues. Hence, our MeMGB-Diff is a state-of-the-art (SOTA) method for 3D bias field correction.

Adaptive immunity is a targeted immune response that enables the body to identify and eliminate foreign pathogens, playing a critical role in the anti-tumor immune response. Tumor cell expression of antigens forms the foundation for inducing this adaptive response. However, the human leukocyte antigens (HLA)-restricted recognition of antigens by T-cell receptors (TCR) limits their ability to detect all neoantigens, with only a small subset capable of activating T-cells. Accurately predicting neoantigen binding to TCR is, therefore, crucial for assessing their immunogenic potential in clinical settings. We present THLANet, a deep learning model designed to predict the binding specificity of TCR to neoantigens presented by class I HLAs. THLANet employs evolutionary scale modeling-2 (ESM-2), replacing the traditional embedding methods to enhance sequence feature representation. Using scTCR-seq data, we obtained the TCR immune repertoire and constructed a TCR-pHLA binding database to validate THLANet's clinical potential. The model's performance was further evaluated using clinical cancer data across various cancer types. Additionally, by analyzing divided complementarity-determining region (CDR3) sequences and simulating alanine scanning of antigen sequences, we provided new insights into the 3D binding interactions of TCRs and antigens. Predicting TCR-neoantigen pairing remains a significant challenge in immunology, THLANet provides accurate predictions using only the TCR sequence (CDR3β), antigen sequence, and class I HLA, offering novel insights into TCR-antigen interactions.

Automatic acne severity grading is crucial for the accurate diagnosis and effective treatment of skin diseases. However, the acne severity grading process is often ambiguous due to the similar appearance of acne with close severity, making it challenging to achieve reliable acne severity grading. Following the idea of fuzzy logic for handling uncertainty in decision-making, we transforms the acne severity grading task into a fuzzy label learning (FLL) problem, and propose a novel adjacency-aware fuzzy label learning (AFLL) framework to handle uncertainties in this task. The AFLL framework makes four significant contributions, each demonstrated to be highly effective in extensive experiments. First, we introduce a novel adjacency-aware decision sequence generation method that enhances sequence tree construction by reducing bias and improving discriminative power. Second, we present a consistency-guided decision sequence prediction method that mitigates error propagation in hierarchical decision-making through a novel selective masking decision strategy. Third, our proposed sequential conjoint distribution loss innovatively captures the differences for both high and low fuzzy memberships across the entire fuzzy label set while modeling the internal temporal order among different acne severity labels with a cumulative distribution, leading to substantial improvements in FLL. Fourth, to the best of our knowledge, AFLL is the first approach to explicitly address the challenge of distinguishing adjacent categories in acne severity grading tasks. Experimental results on the public ACNE04 dataset demonstrate that AFLL significantly outperforms existing methods, establishing a new state-of-the-art in acne severity grading.

Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading to inaccurate or incomplete diagnostic results. In this work, we propose MedFILIP, a fine-grained VLP model, introduces medical image-specific knowledge through contrastive learning, specifically: 1) An information extractor based on a large language model is proposed to decouple comprehensive disease details from reports, which excels in extracting disease deals through flexible prompt engineering, thereby effectively reducing text complexity while retaining rich information at a tiny cost. 2) A knowledge injector is proposed to construct relationships between categories and visual attributes, which help the model to make judgments based on image features, and fosters knowledge extrapolation to unfamiliar disease categories. 3) A semantic similarity matrix based on fine-grained annotations is proposed, providing smoother, information-richer labels, thus allowing fine-grained image-text alignment. 4) We validate MedFILIP on numerous datasets, e.g., RSNA-Pneumonia, NIH ChestX-ray14, VinBigData, and COVID-19. For single-label, multi-label, and fine-grained classification, our model achieves state-of-the-art performance, the classification accuracy has increased by a maximum of 6.69%.

The right atrium (RA) is critical for cardiac hemodynamics but is often overlooked in clinical diagnostics. This study presents a benchmark framework for RA cavity segmentation from late gadolinium-enhanced magnetic resonance imaging (LGE-MRIs), leveraging a two-stage strategy and a novel 3D deep learning network, RASnet. The architecture addresses challenges in class imbalance and anatomical variability by incorporating multi-path input, multi-scale feature fusion modules, Vision Transformers, context interaction mechanisms, and deep supervision. Evaluated on datasets comprising 354 LGE-MRIs, RASnet achieves SOTA performance with a Dice score of 92.19% on a primary dataset and demonstrates robust generalizability on an independent dataset. The proposed framework establishes a benchmark for RA cavity segmentation, enabling accurate and efficient analysis for cardiac imaging applications. Open-source code (https://github.com/zjinw/RAS) and data (https://zenodo.org/records/15524472) are provided to facilitate further research and clinical adoption.

Cardiovascular diseases are a major global health challenge, with electrocardiography (ECG) being critical for diagnosis and monitoring. As artificial intelligence and automated ECG diagnostic technologies rapidly advance, the demand for large-scale ECG databases continues to grow. Generative ECG has become a mainstream method to enhance database size and diversity. However, existing methods typically generate ECG randomly or focus on limited physiological categories, lacking the ability to synthesize ECG with varying physiological features and cardiac cycles, which is crucial for various practical applications. In response to this need, we propose a novel approach introducing a diffusion model called DIFF-ECG to generate precisely customized ECG that accurately reflect diverse cardiac conditions. Segmentation-based quality assessments confirmed that the synthesized ECG accurately followed the specified cardiac cycle information, with our model significantly outperforming baseline diffusion and GAN-based methods. Therefore, our approach addresses the critical need for generating clinically relevant and customizable ECG, contributing significantly to the field of automated cardiac disease diagnosis. By enabling fine-tuning of cardiac cycle phases, our method significantly expands the application range of generative ECG, potentially improving the diagnostic accuracy for rare diseases and advancing personalized medicine.

Medical image registration (MIR) is essential for various clinical diagnoses and treatments. Despite the rapid progress in deep learning-based MIR techniques, most methods focus on directly optimizing the raw image intensity information. In this paper, we explore the usage of edge information of anatomical structures associated with the spatial location of image intensities to assist in registration, termed EdgeReg. The intuition is that the edge information can provide additional rich boundary information to the raw images, enhancing the network’s feature representation. Additionally, as the edge images are strictly spatially consistent with the raw images, additional supervised information can be added to network training. Specifically, we first extract the edge images from the raw moving and fixed images using the Sobel operator and feed these images into a lightweight feature extractor to merge the image intensity and edge information. The enriched features are subsequently input into established registration networks. Finally, similarity loss is applied to both the raw and edge images. Extensive experiments show that EdgeReg is compatible with various networks across diverse datasets and dimensions (2D and 3D), achieving superior registration performance. In particular, EdgeReg does not rely on segmentation labels and is trained in an unsupervised paradigm. Therefore, edge information is a beneficial assistance for unsupervised MIR. The code is available at https://github.com/PerceptionComputingLab/EdgeReg.

Heart failure alters the electrophysiological properties of cardiomyocytes, leading to changes in the overall mechanical function of the heart, with significant implications for human health. Current research on heart failure predominantly focuses on two-dimensional electrophysiological models, which limits their ability to fully capture the complexity of heart failure. In contrast, our work integrates both electrophysio-logical and mechanical aspects by developing a cardiomyocyte model incorporating heart failure remodeling within a three-dimensional electromechanical coupling model, providing a more comprehensive understanding of heart failure dynamics. We investigated the variations in electromechanical coupling properties of the left ventricle under heart failure conditions, focusing on key indicators such as ventricular action potentials, myocardial contractility, ventricular volume, and pressure. The simulation results successfully reproduced the impact of heart failure on both electrophysiological and mechanical characteristics. Additionally, the simulation observed reentrant waves under heart failure conditions, further revealing that heart failure predisposes the heart to reentrant arrhythmias. In conclusion, the 3D electromechanical coupling model presented in this study offers crucial insights into the mechanisms of reentry phenomena and arrhythmogenesis under heart failure conditions.

Recently, self-training strategies have shown outstanding performance in the unsupervised medical image registration field. These strategies use their own network to generate pseudo-displacement fields (PFs) to supervise network training. However, limited diversity and accuracy of these PFs hinder their effectiveness. To address these limitations, we propose a novel mutual learning registration paradigm (MutualReg), where knowledge is distilled mutually between teacher and student networks for alternate improvement via recursive training. This involves two fundamental challenges: 1) how to generate more diverse and accurate PFs; and 2) how to effectively integrate knowledge distillation from the teacher network and learning from the student network. For the former, we employ a different and powerful teacher network thanks to the decoupling nature of MutualReg. For the latter, we introduce a Voxel-wise Reliability Criterion (VRC) module to retain reliable voxel locations of knowledge distillation. In the abdominal CT registration task, MutualReg outperforms state-of-the-art competitors, demonstrating its effectiveness. Code is available from https://github.com/PerceptionComputingLab/MutualReg/.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.

MOTIVATION: Understanding the intermolecular interactions of ligand-target pairs is key to guiding the optimization of drug research on cancers, which can greatly mitigate overburden workloads for wet labs. Several improved computational methods have been introduced and exhibit promising performance for these identification tasks, but some pitfalls restrict their practical applications: (i) first, existing methods do not sufficiently consider how multigranular molecule representations influence interaction patterns between proteins and compounds; and (ii) second, existing methods seldom explicitly model the binding sites when an interaction occurs to enable better prediction and interpretation, which may lead to unexpected obstacles to biological researchers. RESULTS: To address these issues, we here present DrugMGR, a deep multigranular drug representation model capable of predicting binding affinities and regions for each ligand-target pair. We conduct consistent experiments on three benchmark datasets using existing methods and introduce a new specific dataset to better validate the prediction of binding sites. For practical application, target-specific compound identification tasks are also carried out to validate the capability of real-world compound screen. Moreover, the visualization of some practical interaction scenarios provides interpretable insights from the results of the predictions. The proposed DrugMGR achieves excellent overall performance in these datasets, exhibiting its advantages and merits against state-of-the-art methods. Thus, the downstream task of DrugMGR can be fine-tuned for identifying the potential compounds that target proteins for clinical treatment. AVAILABILITY AND IMPLEMENTATION: https://github.com/lixiaokun2020/DrugMGR.

Patients with myocardial ischemia and infarction are at increased risk of arrhythmias, which in turn, can exacerbate the overall risk of mortality. Despite the observed reduction in recurrent arrhythmias through antiarrhythmic drug therapy, the precise mechanisms underlying their effectiveness in treating ischemic heart disease remain unclear. Moreover, there is a lack of specialized drugs designed explicitly for the treatment of myocardial ischemic arrhythmia. This study employs an electrophysiological simulation approach to investigate the potential antiarrhythmic effects and underlying mechanisms of various pharmacological agents in the context of ischemia and myocardial infarction (MI). Based on physiological experimental data, computational models are developed to simulate the effects of a series of pharmacological agents (amiodarone, telmisartan, E-4031, chromanol 293B, and glibenclamide) on cellular electrophysiology and utilized to further evaluate their antiarrhythmic effectiveness during ischemia. On 2D and 3D tissues with multiple pathological conditions, the simulation results indicate that the antiarrhythmic effect of glibenclamide is primarily attributed to the suppression of efflux of potassium ion to facilitate the restitution of [K+]o, as opposed to recovery of IKATP during myocardial ischemia. This discovery implies that, during acute cardiac ischemia, pro-arrhythmogenic alterations in cardiac tissue's excitability and conduction properties are more significantly influenced by electrophysiological changes in the depolarization rate, as opposed to variations in the action potential duration (APD). These findings offer specific insights into potentially effective targets for investigating ischemic arrhythmias, providing significant guidance for clinical interventions in acute coronary syndrome.

The shortage of annotated ECG data presents a significant impediment, hampering the overall generalization capabilities of machine learning models tailored for automated ECG classification. The collective integration of multisource datasets presents a potential remedy for this challenge. However, it is crucial to underscore that the mere addition of supplementary data does not automatically guarantee performance enhancement, given the unresolved challenges associated with multisource data. In this research, we address one such challenge, namely, the issue of incomplete labels arising from the diversity of annotations within multi-source ECG datasets. First, we identified three distinct types of label missing: dataset-related label missing, supertype missing, and subtype missing. To address the supertype missing effectively, we introduce a novel approach known as offline category mapping which leverages the hierarchical relationships inherent within the categories to recover the missing supertype labels. Additionally, two complementary strategies, referred to as prediction masking and online category mapping, are proposed to mitigating the adverse effects of subtype and dataset-related label missing on model optimization. These strategies enhance the model's ability to identify missing subtypes under conditions of weak supervision. These pioneering methodologies are integrated into a deep learning-based framework designed for multilabel ECG classification. The performance of our proposed framework is rigorously evaluated using realistic multi-source datasets obtained from the PhysioNet/CinC challenge 2020/2021. The proposed learning framework exhibits a notable improvement in macro-average precision, surpassing the corresponding baseline model by more than 25 % on the test datasets. As a result, this research study makes a substantial contribution to the field of ECG classification by addressing the critical issue of incomplete labels in multisource datasets, ultimately enhancing the generalization capabilities of machine learning models in this domain.

Previous studies have shown that coronary artery occlusion can cause myocardial ischemia, which can induce ventricular tachycardia or fibrillation. According to the time sequence, myocardial ischemia can be divided into different pathological stages: the ischemia 1a stage (0-15 minutes), the ischemia 1b stage (15-45 minutes), the short-term myocardial infarction (MI) (within a few days) and the long-term MI (within a few weeks). However, few studies give attention to antiarrhythmic drugs directly acting on ion channels for the treatment of myocardial ischemia. The main reason is that the effective targets in myocardial ischemia are unclear. Therefore, based on the technology of electrophysiological simulation, the paper modeled the human ventricular cell model in myocardial ischemia. On this basis, the parameter sensitivity of the model was analyzed and the effective drug targets are selected. Firstly, human ventricular cell models were modeled in ischemia 1a, ischemia 1b, short-term MI and long-term MI based on the experimental data. Then, the sensitivity analysis of output parameters (APA, APD and RP) of the cell model in ischemia was analyzed based on the Sobol method. The results of parameter sensitivity analysis in this paper showed that APD of cell models in ischemia 1a, ischemia 1b and short-term MI was the most sensitive to the change of IKATP, and APA and RP of cells were the most sensitive to the change of [K+]o. The parameter sensitivity analysis of the cell model in long-term MI showed that APD of cell models was sensitive to IKsand ICaL, and APA was most sensitive to INa. Therefore, according to the results of parameter sensitivity analysis, the possible effective targets for the treatment of myocardial ischemia can be preliminarily selected: [K+]o, IKATP, ICaLand INa.

The application of transformer architecture in analyzing whole slide images (WSIs) has become increasingly popular due to its remarkable ability to learn complex associations. Nevertheless, a significant drawback emerges in the multiclass analysis of WSIs. The majority of the transformer-based methods available currently rely primarily on a single, class-agnostic token. This approach might not ideally capture the subtleties of class-discriminative information. To address this challenge, we present an innovative approach tailored for multi-class WSI analysis that harnesses the power of class-specific tokens. Central to our method is a novel attention mechanism designed to foster a synergistic learning relationship between patch and class tokens, enhancing the granularity of information captured and ensuring a more comprehensive representation of the WSI. Complementing this, we introduce a dynamic class-centric training strategy designed to optimize token representation learning, ensuring each token is informatively aligned with its corresponding class. Through extensive experimentation on three challenging multi-class WSI analysis datasets, our method consistently demonstrates superior performance, underscoring its potential as a robust solution for multi-class WSI analysis tasks.

Neoadjuvant chemoradiotherapy (nCRT) is the stan-dard treatment for locally advanced rectal cancer (LARC). With the development of artificial intelligence, an increasing number of studies have begun to explore its application in cancer treatment prediction. However, the prior methods exhibit considerable variability even with slight modifications to the input data, which could potentially undermine the reliability of the results. In this paper, we proposed RP-Net, a novel multi-modal fusion-based framework that combines feature information from magnetic resonance imaging (MRI) and whole slide images (WSI), establishing a relationship to map the therapeutic effectiveness of nCRT for LARC. We investigated the relationship of the tumour region and its periphery tissues, and demonstrated the validity of the proposed framework that involving 11 different combinations of modalities. The experimental results revealed that it has achieved higher prediction accuracy compared to the four intra-categories single-modal combinations and outperformed the two intra-categories multi-modal combinations. When compared to the other four inter-categories multi-modal combinations, the fusion features get accuracy of 2 % ~ 6% improvement respectively.

Breast cancer continues to be a pressing global health concern, emphasizing the essential need for effective diagnostic techniques. Automated Breast Ultrasound Systems (ABUS) provide a promising advance in breast tumor detection, yet they require significant expertise in interpreting 3D ABUS images, a task fraught with distinctive challenges. Although Vision Transformers (ViT) display remarkable potential for image processing, their low inductive bias and significant data requirements pose obstacles, particularly in the data-constrained medical field. To mitigate these issues, we introduce a Mask-Recover strategy for pretraining Transformer models on 3D ABUS images, enhancing model adaptability and reducing the data demands of the ViT model. Moreover, recognizing the risk that ViTs’ average pooling approach may unintentionally mask small but vital features, we propose Dual-CapsViT, an inventive model combining Transformers and Capsule Networks. This integration affords efficient token routing while preserving fine-grained details. To reconcile potential inconsistencies between capsules and tokens, we engineer a novel dual-channel routing algorithm, strengthening the decoder’s performance. We benchmarked our models against well-known standards such as ResNet and ViT for classifying breast tumors in ABUS images. Our models exhibited superior performance, as evidenced by improved accuracy, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC) metrics, thereby affirming Dual-CapsViT’s potential to enhance breast cancer diagnostics.

Guidewire Artifact Removal (GAR) involves restoring missing imaging signals in areas of IntraVascular Optical Coherence Tomography (IVOCT) videos affected by guidewire artifacts. GAR helps overcome imaging defects and minimizes the impact of missing signals on the diagnosis of CardioVascular Diseases (CVDs). To restore the actual vascular and lesion information within the artifact area, we propose a reliable Trajectory-aware Adaptive imaging Clue analysis Network (TAC-Net) that includes two innovative designs: (i) Adaptive clue aggregation, which considers both texture-focused original (ORI) videos and structure-focused relative total variation (RTV) videos, and suppresses texture-structure imbalance with an active weight-adaptation mechanism; (ii) Trajectory-aware Transformer, which uses a novel attention calculation to perceive the attention distribution of artifact trajectories and avoid the interference of irregular and non-uniform artifacts. We provide a detailed formulation for the procedure and evaluation of the GAR task and conduct comprehensive quantitative and qualitative experiments. The experimental results demonstrate that TAC-Net reliably restores the texture and structure of guidewire artifact areas as expected by experienced physicians (e.g., SSIM: 97.23%). We also discuss the value and potential of the GAR task for clinical applications and computer-aided diagnosis of CVDs.

Biological pacemaker is a therapy for cardiac rhythm disease, which can be transformed from ventricular myocytes (VMs) by overexpressing HCN gene which codes the expression of hyperpolarization-activated current (${\mathrm {I}}_{\mathrm{f}}$) and knocking off Kir2.1 gene which codes inward-rectifier potassium current (${\mathrm {I}}_{\mathrm{K1}}$). Our previous study built a biological pacemaker single cell model and clarified the underlying mechanisms of how gene expressing levels influence the pacemaking activity of single pacemaker cell. But the pacemaking ability of pacemaker tissue has not been researched systematically. And what factors may have effects on pacemaker’s synchronization and spontaneous beating propagation are not clear. Biological research indicated that both sinoatrial node and pacemaker cells has less expression of connexin than unexcitable cardiac cells, which provides a possibility that improve pacemaking ability of pacemaker by decreasing its cell coupling. Another possible factor is the number of pacemaker cells. According to the common sense, increasing cell number can promote pacemaking behaviours, but overmuch pacemaker cells is unreasonable in clinic. As a result, the balance between pacemaker number and cell coupling is important when applying biological pacemaker. In this study, we constructed a two-dimensional cardiac tissue model with the description of electrophysiology to illustrate the relationship between gap junction and cell number. Based on this model, we modified the cell coupling between pacemaker cells by adjusting the diffusion coefficient of tissue with different pacemaker number. In different condition, the synchronization, pacemaking cycle length and electrical signal propagation were evaluated. It can be concluded that weakening cell coupling among pacemaker cells can lift the efficiency of bio-pacemaker therapy. This study may contribute to produce effective pacemaker in clinic.

Liver cancer is the second leading cause of death all over the world in the 2020s’, and the incidence rate has been growing on a global scale and become a serious threat to human life. Early diagnosis of liver cancer from medical images can allow the patients to receive better treatment and achieve good outcomes. Although medical imaging approaches have made significant progress over the past decades, there are still great demands to reconstruct the network structure for the adaptation to downstream tasks. It remains a great challenge for liver tumor identification from MRI images. Recently, self-attention mechanism based transformer models can capture long-range dependencies, which make them perform well on many medical image analysis tasks. Such as Segformer and TransUNet, since lacking the translation in-variance and inductive bias of CNNs, they are still needed large-scale training to fill the gap, especially in the field of medical image analysis domain. In this study, we incorporate a novel flexible Condeathblock as a feature extractor to perform liver images analysis and propose a new analytical framework CXNet to extract discriminative multi-scale visual representations. The experiments results demonstrated our framework outperforms the state-of the-art models on three datasets, including a publicly available liver segmentation dataset as well as two in-house liver tumor classification/segmentation datasets. On the 3DIRCADb dataset, our CXNet outperforms the UNet model by 2.82%, 2.73%, and 4.46% in terms of the Jaccard similarity coefficient, Dice coefficient, and accuracy, and outperforms the TransUNet model by 8.49%, 5.35%,which 3.62%, respectively. Code is available at https://github.com/SPECTRELWF/CXNet

Myocardial ischemia, injury and infarction (MI) are the three stages of acute coronary syndrome (ACS). In the past two decades, a great number of studies focused on myocardial ischemia and MI individually, and showed that the occurrence of reentrant arrhythmias is often associated with myocardial ischemia or MI. However, arrhythmogenic mechanisms in the tissue with various degrees of remodeling in the ischemic heart have not been fully understood. In this study, biophysical detailed single-cell models of ischemia 1a, 1b, and MI were developed to mimic the electrophysiological remodeling at different stages of ACS. 2D tissue models with different distributions of ischemia and MI areas were constructed to investigate the mechanisms of the initiation of reentrant waves during the progression of ischemia. Simulation results in 2D tissues showed that the vulnerable windows (VWs) in simultaneous presence of multiple ischemic conditions were associated with the dynamics of wave propagation in the tissues with each single pathological condition. In the tissue with multiple pathological conditions, reentrant waves were mainly induced by two different mechanisms: one is the heterogeneity along the excitation wavefront, especially the abrupt variation in conduction velocity (CV) across the border of ischemia 1b and MI, and the other is the decreased safe factor (SF) for conduction at the edge of the tissue in MI region which is attributed to the increased excitation threshold of MI region. Finally, the reentrant wave was observed in a 3D model with a scar reconstructed from MRI images of a MI patient. These comprehensive findings provide novel insights for understanding the arrhythmic risk during the progression of myocardial ischemia and highlight the importance of the multiple pathological stages in designing medical therapies for arrhythmias in ischemia.

Accurate segmentation of the Intracranial Hemorrhage (ICH) in non-contrast CT images is significant for computer-aided diagnosis. Although existing methods have achieved remarkable 1 1 The code will be available from https://github.com/JohnleeHIT/SLEX-Net. results, none of them incorporated ICH's prior information in their methods. In this work, for the first time, we proposed a novel SLice EXpansion Network (SLEX-Net), which incorporated hematoma expansion in the segmentation architecture by directly modeling the hematoma variation among adjacent slices. Firstly, a new module named Slice Expansion Module (SEM) was built, which can effectively transfer contextual information between two adjacent slices by mapping predictions from one slice to another. Secondly, to perceive contextual information from both upper and lower slices, we designed two information transmission paths: forward and backward slice expansion, and aggregated results from those paths with a novel weighing strategy. By further exploiting intra-slice and inter-slice context with the information paths, the network significantly improved the accuracy and continuity of segmentation results. Moreover, the proposed SLEX-Net enables us to conduct an uncertainty estimation with one-time inference, which is much more efficient than existing methods. We evaluated the proposed SLEX-Net and compared it with some state-of-the-art methods. Experimental results demonstrate that our method makes significant improvements in all metrics on segmentation performance and outperforms other existing uncertainty estimation methods in terms of several metrics.

Cardiovascular obstruction could lead to myocardial ischemia and myocardial infarction (MI). MI can be further divided into short-term MI stage (several days) and long-term MI stage (several months) with the development of coronary artery obstruction, and the electrophysiological characteristics in these two MI stages vary greatly. At present, there are no relevant studies on the effects of different infarct areas (size and location) on the initialization and maintenance of reentrant waves in these two MI stages. Therefore, this study aims to investigate the differences in vulnerability to reentry between these two MI stages by computer modeling and simulation. Firstly, single cell models, based on the TP06 model were developed in two different MI stages. And simulation results on single-cells showed that the action potential duration (APD) significantly shortened and the resting potential (RP) elevated in the short-term MI stage, compared with that in the normal condition. However, APD prolonged and RP only changed little in the long-term MI stage. When MI areas in 2D annular ventricular tissues were designed with different lengths, widths and positions, the distribution of the vulnerable window (VW) in these two MI stages was investigated. The simulation results showed that the vulnerability of the two MI stages to the length and position of the infarct areas is the same. That is with the increase of the length, VW gradually increased and reached a constant value when the percentage of the length of the MI area reached 50%. And VW was the largest when the infarct area was close to the inner or outer wall. The vulnerability to the width of the infarct area in these two MI stages is different. In short-term MI, VW was the largest when the width of the infarct area was narrow or wide, while in long-term MI, VW was the largest when the width of the MI area reached half of the width of the ventricular wall. In this paper, the effect of the different infarct areas on the initialization and maintenance of reentrant waves in two different MI stages was investigated by computing simulation. This would improve the understanding of arrhythmogenicity in the MI stage and could provide new sights in arrhythmogenic mechanism of MI phases.

Myocardial ischemia and myocardial infarction (MI) are often accompanied by the occurrence of reentrant arrhythmias, which may lead to sudden cardiac death in severe cases. Previous studies show that electrical alternans can occur during myocardial ischemia and MI and may lead to arrhythmias. However, so far, the mechanism of alternans during myocardial ischemia and MI is unclear, so the related study on alternans is particularly important. Based on single-cell models previously modeled by us at three stages: ischemia 1a, 1b, and MI, the mechanism of alternans was revealed by comparing the changes in alternans at three levels: single cells, one-dimensional (1D) tissues, and two-dimensional (2D) tissues. In addition, the main factors inducing alternans were investigated, and the effect of antiarrhythmic drug glibenclamide on alternans was simulated. The simulation results on single cells of ischemia 1a, 1b and MI showed that the electrical alternans on the cell-levels were unstable electrical alternans. Simulation results in tissues showed that stable electrical alternans could occur in both 1D and 2D tissues. Simulation results showed that alternans in ischemia 1a were mainly caused by two factors: inhibition of $\mathrm{I}_{\mathrm{Na}}$ and elevation of $[\mathrm{K}^{+}]_{\mathrm{o}}$; alternans in ischemia 1b were mainly caused by two factors: inhibition of $\mathrm{I}_{\mathrm{NaK}}$ and elevation of $[\mathrm{K}^{+}]_{\mathrm{o}}$; alternans in MI were mainly caused by three factors: inhibition of $\mathrm{I}_{\mathrm{Kr}}$, inhibition of $\mathrm{I}_{\mathrm{Ks}}$, and elevation of $[\mathrm{K}^{+}]_{\mathrm{o}}$. And electrical alternans in the tissues result in a 2:1 conduction block. In addition, the simulation results showed that glibenclamide could inhibit electrical alternans in single cells and tissues. Electrical alternans during ischemia 1a, 1b and MI are caused by several currents that directly affect the action potential, and can lead to a 2:1 conduction block in tissues. Glibenclamide inhibits the occurrence of electrical alternans by inhibiting the efflux of potassium ions.

Abstract Authentication is the process of verifying the claimed identity of the user. Recently, traditional authentication methods such as passwords, tokens, and so on are no longer used for authentication as they are more prone to theft and different types of violations. Therefore, new authentication approaches based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals are considered. Unlike other biometrics, ECG provides the assurance that the person is alive, and is considered as one of the most accurate recent methods for authentication. In this article, two end‐to‐end deep neural network models for ECG‐based authentication are proposed. In the first model, a convolutional neural network (CNN) is developed and in the second model, a residual convolutional neural network (ResNet) with attention mechanism called ResNet‐Attention is designed for human authentication. We have used 2‐s duration ECG signals obtained from two ECG databases (Physikalisch‐Technische Bundesanstalt [PTB] and Check Your Bio‐signals Here initiative [CYBHi]) for authentication. Our proposed ResNet‐Attention algorithm achieved an accuracy of 98.85 and 99.27% using PTB and CYBHi, respectively. The results obtained by our developed model show that the performance is better than existing algorithms and can be used in real‐time authentication systems after the validation with more diverse ECG data.

Pacemaking dysfunction (PD) may result in heart rhythm disorders, syncope or even death. Current treatment of PD using implanted electronic pacemakers has some limitations, such as finite battery life and the risk of repeated surgery. As such, the biological pacemaker has been proposed as a potential alternative to the electronic pacemaker for PD treatment. Experimentally and computationally, it has been shown that bio-engineered pacemaker cells can be generated from non-rhythmic ventricular myocytes (VMs) by knocking out genes related to the inward rectifier potassium channel current (IK1) or by overexpressing hyperpolarization-activated cyclic nucleotide gated channel genes responsible for the "funny" current (If). However, it is unclear if a bio-engineered pacemaker based on the modification of IK1- and If-related channels simultaneously would enhance the ability and stability of bio-engineered pacemaking action potentials. In this study, the possible mechanism(s) responsible for VMs to generate spontaneous pacemaking activity by regulating IK1 and If density were investigated by a computational approach. Our results showed that there was a reciprocal interaction between IK1 and If in ventricular pacemaker model. The effect of IK1 depression on generating ventricular pacemaker was mono-phasic while that of If augmentation was bi-phasic. A moderate increase of If promoted pacemaking activity but excessive increase of If resulted in a slowdown in the pacemaking rate and even an unstable pacemaking state. The dedicated interplay between IK1 and If in generating stable pacemaking and dysrhythmias was evaluated. Finally, a theoretical analysis in the IK1/If parameter space for generating pacemaking action potentials in different states was provided. In conclusion, to the best of our knowledge, this study provides a wide theoretical insight into understandings for generating stable and robust pacemaker cells from non-pacemaking VMs by the interplay of IK1 and If, which may be helpful in designing engineered biological pacemakers for application purposes.

OBJECTIVE: Detecting changes in the QRS complexes in ECG signals is regarded as a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach for evaluating the cardiac health of patients. Therefore, detecting QRS complexes in ECG signals must be accurate over short times. However, the reliability of automatic QRS detection is restricted by all kinds of noise and complex signal morphologies. The objective of this paper is to address automatic detection of QRS complexes. METHODS: In this paper, we proposed a new algorithm for automatic detection of QRS complexes using dual channels based on U-Net and bidirectional long short-term memory. First, a proposed preprocessor with mean filtering and discrete wavelet transform was initially applied to remove different types of noise. Next the signal was transformed and annotations were relabeled. Finally, a method combining U-Net and bidirectional long short-term memory with dual channels was used for the automatic detection of QRS complexes. RESULTS: The proposed algorithm was trained and tested using 44 ECG records from the MIT-BIH arrhythmia database and CPSC2019 dataset, which achieved 99.06% and 95.13% for sensitivity, 99.22% and 82.03% for positive predictivity, and 98.29% and 78.73% accuracy on the two datasets respectively. CONCLUSION: Experimental results prove that the proposed method may be useful for automatic detection of QRS complex task. SIGNIFICANCE: The proposed method not only has application potential for QRS complex detecting for large ECG data, but also can be extended to other medical signal research fields.

Sinus node ischemia is mainly characterized by slow heart rate, which is caused by ischemia-induced changes in electrophysiological properties and ion homeostasis leading to prolonged pacing cycle length. According to the available data, research on the mechanism of sinus node ischemia has been reported, but the report on the drug treatment of the disease is scarce. In order to reveal the effect of Chinese medicine (Yiqi Tongyang) in sinus node ischemia, this paper uses rabbit sinus node center and periphery models to simulate the effects of medium and high doses of the Chinese medicine in ischemia at the sub-cellular, cellular and tissue levels, and to simulate the changes in cellular action potentials and tissue pacing functions to predict the drug efficacy. Simulation results showed that 1) the Chinese medicine can effectively shorten the pacing cycle, with negligible effect on the duration of cellular action potential and maximal diastolic potential, but can cause a decrease in the maximal depolarization velocity; 2) it accelerated the activation of ischemic sinus node-atrum tissue, so that the electrical excitatory activity of the tissue return to the normal state; 3) by comparing the simulation results of medium and high doses of the Chinese medicine, it was shown that the high dose group did not increase the heart rate significantly, while the medium dose group had a significant effect on the regulation of heart rate, indicating that the Chinese medicine (Yiqi Tongyang) can effectively treat sinus node ischemic disease.

Sinus node ischemia is mainly characterized by slow heart rate, which is caused by ischemia-induced changes in electrophysiological properties and ion homeostasis leading to prolonged pacing cycle length. According to the available data, research on the mechanism of sinus node ischemia has been reported, but the report on the drug treatment of the disease is scarce. In order to reveal the effect of Chinese medicine (Yiqi Tongyang) in sinus node ischemia, this paper uses rabbit sinus node center and periphery models to simulate the effects of medium and high doses of the Chinese medicine in ischemia at the sub-cellular, cellular and tissue levels, and to simulate the changes in cellular action potentials and tissue pacing functions to predict the drug efficacy. Simulation results showed that 1) the Chinese medicine can effectively shorten the pacing cycle, with negligible effect on the duration of cellular action potential and maximal diastolic potential, but can cause a decrease in the maximal depolarization velocity; 2) it accelerated the activation of ischemic sinus node-atrum tissue, so that the electrical excitatory activity of the tissue return to the normal state; 3) by comparing the simulation results of medium and high doses of the Chinese medicine, it was shown that the high dose group did not increase the heart rate significantly, while the medium dose group had a significant effect on the regulation of heart rate, indicating that the Chinese medicine (Yiqi Tongyang) can effectively treat sinus node ischemic disease.

The biological pacemaker was a promising therapy for cardiac diseases such as sick sinus syndrome and atrioventricular block. A lot of experiments showed that pacemaker cells can be transformed from non-rhythmic cardiac cells or stem cells by gene therapy. However, at the tissue level, the electrophysiological properties between rhythmic and non-rhythmic regions are different. For example, the expression of connexin (such as Cx43) reduced in the induced-pacemaker cells which means that the pacemaker cells may have a less electrical coupling with adjacent cells. In addition, some researches indicated that the spatial distribution of pacemaker cells influenced the excitability of cardiac tissue. To the best of our knowledge, it is still unclear how the spatial distribution of bio-pacemaker cells affects the pacemaking behaviour in biological pacemaker tissue. In this study, we constructed a series of two-dimensional pacemaker-ventricle models containing different distributions of pacemaker cells to investigate the effect of spatial distribution on the pacemaking behaviour. Three kinds of models were designed in our simulations: (1) Tight model; (2) Embedded model; (3) Electrically isolated model. The pacemaking ability of cardiac tissue was measured by the least ratio of pacemaker cells needed to drive the whole tissue. Simulation results showed that electrically isolated model was the optimal model as it showed the best pacemaking ability among these three models. This study may guide the clinical use of bio-pacemaker.

The biological pacemaker was a promising therapy for cardiac diseases such as sick sinus syndrome and atrioventricular block. A lot of experiments showed that pacemaker cells can be transformed from non-rhythmic cardiac cells or stem cells by gene therapy. However, at the tissue level, the electrophysiological properties between rhythmic and non-rhythmic regions are different. For example, the expression of connexin (such as Cx43) reduced in the induced-pacemaker cells which means that the pacemaker cells may have a less electrical coupling with adjacent cells. In addition, some researches indicated that the spatial distribution of pacemaker cells influenced the excitability of cardiac tissue. To the best of our knowledge, it is still unclear how the spatial distribution of bio-pacemaker cells affects the pacemaking behaviour in biological pacemaker tissue. In this study, we constructed a series of two-dimensional pacemakerventricle models containing different distributions of pacemaker cells to investigate the effect of spatial distribution on the pacemaking behaviour. Three kinds of models were designed in our simulations: (1) Tight model; (2) Embedded model; (3) Electrically isolated model. The pacemaking ability of cardiac tissue was measured by the least ratio of pacemaker cells needed to drive the whole tissue. Simulation results showed that electrically isolated model was the optimal model as it showed the best pacemaking ability among these three models. This study may guide the clinical use of biopacemaker.

Heart failure (HF) is associated with an increased propensity for atrial fibrillation (AF), causing higher mortality than AF or HF alone. It is hypothesized that HF-induced remodelling of atrial cellular and tissue properties promotes the genesis of atrial action potential (AP) alternans and conduction alternans that perpetuate AF. However, the mechanism underlying the increased susceptibility to atrial alternans in HF remains incompletely elucidated. In this study, we investigated the effects of how HF-induced atrial cellular electrophysiological (with prolonged AP duration) and tissue structural (reduced cell-to-cell coupling caused by atrial fibrosis) remodelling can have an effect on the generation of atrial AP alternans and their conduction at the cellular and one-dimensional (1D) tissue levels. Simulation results showed that HF-induced atrial electrical remodelling prolonged AP duration, which was accompanied by an increased sarcoplasmic reticulum (SR) Ca2+ content and Ca2+ transient amplitude. Further analysis demonstrated that HF-induced atrial electrical remodelling increased susceptibility to atrial alternans mainly due to the increased sarcoplasmic reticulum Ca2+-ATPase (SERCA) Ca2+ reuptake, modulated by increased phospholamban (PLB) phosphorylation, and the decreased transient outward K+ current (Ito). The underlying mechanism has been suggested that the increased SR Ca2+ content and prolonged AP did not fully recover to their previous levels at the end of diastole, resulting in a smaller SR Ca2+ release and AP in the next beat. These produced Ca2+ transient alternans and AP alternans, and further caused AP alternans and Ca2+ transient alternans through Ca2+→AP coupling and AP→Ca2+ coupling, respectively. Simulation of a 1D tissue model showed that the combined action of HF-induced ion channel remodelling and a decrease in cell-to-cell coupling due to fibrosis increased the heart tissue's susceptibility to the formation of spatially discordant alternans, resulting in an increased functional AP propagation dispersion, which is pro-arrhythmic. These findings provide insights into how HF promotes atrial arrhythmia in association with atrial alternans.

It has been verified that biological pacemaker could be produced based on ventricular myocytes (VMs) by overexpressing HCN gene which codes the expression of hyperpolarization-activated current (If). Clinically, xenograft is in common use by which one specie' stem cell is infected with another specie's HCN gene so that the stem cell could transfer into cardiac pacemaker cell. The difference of HCN gene between species affects Ifproperties, but how the Ifproperties influence pacemaker creation is not easy to be qualified in biological experiments. In this study, we build an engineered biological pacemaker model based on a ventricular myocyte model by incorporating Ifformulation and simulated the membrane potential of biological pacemaker. The Ifof different species is simulated by modifying average half-maximal activation voltage (V1/2) of Ifactivation gate and Ifconductance (Gf). Based on the modified pacemaker model, the effect of Ifproperties on pacemaking stability and frequency is evaluated. Simulation results indicate that pacemaking ability is influenced dramatically by Ifproperties. In addition, the spontaneous pacemaking mechanism showed both membrane-clock and Ca2+-clock and its deep reason is analyzed in this paper. This study may provide a subcellular perspective for the clinical use of biological pacemaker.

Segmentation of brain tumors from magnetic resonance imaging (MRI) data sets is of great importance for improved diagnosis, growth rate prediction, and treatment planning. However, automating this process is challenging due to the presence of severe partial volume effect and considerable variability in tumor structures, as well as imaging conditions, especially for the gliomas. In this paper, we introduce a new methodology that combines random forests and active contour model for the automated segmentation of the gliomas from multimodal volumetric MR images. Specifically, we employ a feature representations learning strategy to effectively explore both local and contextual information from multimodal images for tissue segmentation by using modality specific random forests as the feature learning kernels. Different levels of the structural information is subsequently integrated into concatenated and connected random forests for gliomas structure inferring. Finally, a novel multiscale patch driven active contour model is exploited to refine the inferred structure by taking advantage of sparse representation techniques. Results reported on public benchmarks reveal that our architecture achieves competitive accuracy compared to the state-of-the-art brain tumor segmentation methods while being computationally efficient.

Motivation: Long QT syndromes (LQTS) are characterized by early after depolarizations (EADs), repolarization dispersion and tachyarrhythmias. However, mechanisms by which these substrates promote tachyarrhythmias remain to be fully elucidated. This study sought to test the hypothesis that EADs induced by abrupt rate accelerations can occur and investigate how this abrupt rate accelerations is related to the mechanisms of reentrant excitations.Methods: The TP06 model for human ventricular cell was modified to model experimental conditions in LQTS. Then, the normal and EADs cell models were incorporated into homogeneous multicellular 1D and 2D tissue models to study the mechanism underlying the generation of reentrant events. Results and conclusions: In single cell simulations, abrupt accelerations in the heart rate prolonged action potential duration and favored to the genesis of EADs. In the ID simulations, an EADs region increased tissue's vulnerability to unidirectional conduction block in response to abrupt rate accelerations. In the 2D idealized tissue simulations, abrupt rate accelerations induced initiation of spiral waves due to an increase in repolarization gradients caused by an EADs region. These computer simulations suggest that abrupt rate accelerations can favor to the genesis of EADs and an EADs region can enhance the susceptibility of arrhythmias by increasing dispersion of repolarization. Thus, the increased regional repolarization dispersion caused by abrupt rate accelerations is a primary factor that may primarily contribute to the genesis of tachyarrhythmias in LQTS.

In this paper, we proposed a novel left ventricular volumes prediction method. This method is a cascade architecture which is based on multi-scale LV atlas location and deep convolutional neural networks (CNN). Firstly, we adopted LV atlas mapping method to achieve accurate location of LV region in cardiac magnetic resonance (CMR) images. And then, the CNN were used to train an end-to-end LV volumes prediction model to achieve the direct prediction. What's more, the large number of CMR images data (1140 subjects, more than 1026000 images) make the proposed deep CNN have relatively better feature representation and robust prediction ability. The experiment results on the large-scale CMR datasets prove that the proposed method has higher accuracy than the state-of-the-art prediction methods in terms of the end-diastole volumes (EDV), the end-systole volumes (ESV), and the ejection fraction (EF). Besides, we make the proposed method open accessible to public for wide application in other biomedical image processing fields.

The identified genetic short QT syndrome (SQTS) is associated with an increased risk of arrhythmia and sudden death. This study was to investigate the potential effects of propafenone on KCNH2-linked short QT syndrome (SQT1) using a multi-scale biophysically detailed model of the heart developed by ten Tusscher and Panfilov. The ion electrical conductivities were reduced by propafenone in order to simulate the pharmacological effects in healthy and SQT1 cells. Based on the experimental data of McPate et al., the pharmacological effect of propafenone was modelled by dose-dependent IKrblocking. Action potential (AP) profiles and 1D tissue level were analyzed to predict the effects of propafenone on SQT1. Both low- and high- dose of propafenone prolonged APD and QT interval in SQT1 cells. It suggests the superior efficacy of high dose of propafenone on SQT1. However, propafenone did not significantly alter the healthy APD or QT interval at low dose, whereas markedly shortened them at high dose. Our simulation data show that propafenone has a dose-dependently anti-arrhythmic effect on SQT1, and a pro-arrhythmic effect on healthy cells. These computer simulations help to better understand the underlying mechanisms responsible for the initiation or termination of arrhythmias in healthy or SQT1 patients using propafenone.

Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view images is a fundamental yet active research area in computer vision. Despite the steady progress in multi-view stereo (MVS) reconstruction, many existing methods are still limited in recovering fine-scale details and sharp features while suppressing noises, and may fail in reconstructing regions with less textures. To address these limitations, this paper presents a detail-preserving and content-aware variational (DCV) MVS method, which reconstructs the 3D surface by alternating between reprojection error minimization and mesh denoising. In reprojection error minimization, we propose a novel inter-image similarity measure, which is effective to preserve fine-scale details of the reconstructed surface and builds a connection between guided image filtering and image registration. In mesh denoising, we propose a content-aware ℓp-minimization algorithm by adaptively estimating the p value and regularization parameters. Compared with conventional isotropic mesh smoothing approaches, the proposed method is much more promising in suppressing noise while preserving sharp features. Experimental results on benchmark data sets demonstrate that our DCV method is capable of recovering more surface details, and obtains cleaner and more accurate reconstructions than the state-of-the-art methods. In particular, our method achieves the best results among all published methods on the Middlebury dino ring and dino sparse data sets in terms of both completeness and accuracy.

Transcription factor TBX18 could reduce the electrical coupling of ventricular myocytes and slow the electrical propagation, leading to pacemaker activity. In this article, the effect of TBX18 was analyzed by modulating coupling conductance (diffusion coefficient) and we found that with the decreasing of coupling, the pacemaker activity of ventricle increased. The first pacing time decreased with the reduction of coupling. However, when coupling conductance was lower than a critical value, the automatic excitation could not propagate, although the pacemaker worked robustly. Once the working myocytes could be driven, the pacemakers with different coupling conductance made no significant difference. Action potentials (APs) of pacemaker cells and normal cardiac myocytes at the same coordinates were similar for different coupling.

BACKGROUND: Reconstruction of neuron anatomy structure is a challenging and important task in neuroscience. However, few algorithms can automatically reconstruct the full structure well without manual assistance, making it essential to develop new methods for this task. METHODS: This paper introduces a new pipeline for reconstructing neuron anatomy structure from 3-D microscopy image stacks. This pipeline is initialized with a set of seeds that were detected by our proposed Sliding Volume Filter (SVF), given a non-circular cross-section of a neuron cell. Then, an improved open curve snake model combined with a SVF external force is applied to trace the full skeleton of the neuron cell. A radius estimation method based on a 2D sliding band filter is developed to fit the real edge of the cross-section of the neuron cell. Finally, a surface reconstruction method based on non-parallel curve networks is used to generate the neuron cell surface to finish this pipeline. RESULTS: The proposed pipeline has been evaluated using publicly available datasets. The results show that the proposed method achieves promising results in some datasets from the DIgital reconstruction of Axonal and DEndritic Morphology (DIADEM) challenge and new BigNeuron project. CONCLUSION: The new pipeline works well in neuron tracing and reconstruction. It can achieve higher efficiency, stability and robustness in neuron skeleton tracing. Furthermore, the proposed radius estimation method and applied surface reconstruction method can obtain more accurate neuron anatomy structures.

Cisapride as a prokinetic drug inhibits rapid delayed rectifier potassium channel current. As producing QT interval prolongation and causes fatal cardiac arrhythmias, it has been withdrawn from clinical uses. However, exact mechanisms for the proarrhythmic effects of cisapride are incompletely unclear. In this study, we implemented a biophysically detailed computational model of the heart to quantify the effects of the cisapride on cardiac electrical activities at cellular and tissue levels, from which we analyzed the proarrhythmic effects of the agent.

Turning non-autonomic ventricular cells into pacemaking cells is believed to hold the key for making a bio-pacemaker that could potentially treat patients with cardiac conduction diseases. In this article, we analyze the effects of various membrane ion channel currents on ventricular automaticity induced by reducing the inward-rectifier K+current (IK1). It was found that the L-type calcium current (ICaL), rather than the fast sodium current (INa), plays a major role in the rapid depolarization phase of the action potential. With a small ICaL, the automaticity of cells failed due to incompletion of the rapid depolarization. However, during the slow depolarization phase of the action potential, the background sodium current (IbNa), background calcium current (IbCa) and Na+/Ca2+exchanger current (INaCa) were playing more important roles. In 2D simulations, the automatic ventricular excitations arising from IK1reduction only couldn't propagate; it required other currents to be modulated at the same time for driving the surrounding cardiac tissues.

Automatic neuron reconstruction is one of the foremost challenging and important problem in the field of neuroscience. However, none of the prevalent algorithms can automatically reconstruct full anatomy structure. All of these make it is essential of developing new method for the tracing task. This paper introduced a novel seeding method for reconstructing neuron structures from 3-D microscopy images stacks. The protocol was initialized with a set of seeds which were detected by our proposed Sliding Volume Filter. And then the open curve snake was applied to the detected seeds to reconstruct the full structural of neuron cells. Results showed the proposed method exhibited excellent performance with its accuracy compared with traditional method. It is worth noting that the seeding method can clearly benefit for 3-D neuron fiber detection and reconstruction.

Hodgkin-Huxley(HH) equation is a classical model in electrophysiology and has been studied by many scholars. Applying stability theory, and taking maximal sodium conductance g̅naand potassium conductance g̅kas variables, in this study we analyze the stability and bifurcations of the model. Bifurcations are found when the variables change, and bifurcation points and boundary are calculated. When g̅nais the variable, there is only one bifurcation point and there are two points when g̅kis variable. The (g̅na, g̅k) plane is partitioned into two regions and the upper bifurcation boundary is similar to a line when both g̅naand g̅kare variables. The results gotten could be a help to control relevant diseases caused by maximal conductance anomaly.

We present an automatic method for fitting multiple B-spline curves to unorganized planar points. The method works on point clouds which have complicated topological structures and a single curve is insufficient for fitting the shape. A divide-and-merge algorithm is developed for dividing the unorganized data points into several groups while each group represents a smooth curve. Each point group is then fitted with a B-spline curve by the SDM method. Our algorithm also sets up automatically the control polygon of initial B-spline curves. Experiments demonstrate the capability of the presented algorithm in accurate reconstruction of topological structures of point clouds.

Iris analysis studies the relationship between human health and changes in the anatomy of the iris. Apart from the fact that iris recognition focuses on modeling the overall structure of the iris, iris diagnosis emphasizes the detecting and analyzing of local variations in the characteristics of irises. This paper focuses on studying the geometrical structure changes in irises that are caused by gastrointestinal diseases, and on measuring the observable deformations in the geometrical structures of irises that are related to roundness, diameter and other geometric forms of the pupil and the collarette. Pupil and collarette based features are defined and extracted. A series of experiments are implemented on our experimental pathological iris database, including manual clustering of both normal and pathological iris images, manual classification by non-specialists, manual classification by individuals with a medical background, classification ability verification for the proposed features, and disease recognition by applying the proposed features. The results prove the effectiveness and clinical diagnostic significance of the proposed features and a reliable recognition performance for automatic disease diagnosis. Our research results offer a novel systematic perspective for iridology studies and promote the progress of both theoretical and practical work in iris diagnosis.

Content-based document image retrieval is a new and promising research area. Without OCR, document indexing directly based on image content is more general and convenient. However content-based Chinese document retrieval is difficult for the complexity of Chinese character structure and large class numbers. Few papers cover this issue, and this paper will focus on it. This paper presents a novel algorithm of knowledge-based clustering and gives a mechanism of serial batch clustering for large data set. Knowledge derives from an artificial document image collection. Chinese characters with high frequency are edited and synthesized to images automatically. Cluster IDs are adopted to index the characters. A Dream of Red Mansions, a famous classical Chinese literature work including near one million characters, is used to evaluate the performance of Chinese keyword spotting. Experimental results confirm the effectiveness of knowledge-based clustering and its application on Chinese keyword spotting.

This paper tries to fill the gap between Traditional Chinese Pulse Diagnosis (TCPD) and Doppler diagnosis by applying digital signal analysis and pattern classification techniques to wrist radial arterial Doppler blood flow signals. Doppler blood flows signals (DBFS) of patients with cholecystitis, gastritis and healthy people are classified by L2-soft margin SVM and 5 linear classifiers using the proposed feature - piecewise axially integrated bispectra (PAIB). A 5-fold cross validation is used for performance evaluation. The classification accuracies between either two groups of subjects are greater than 93%. Gastritis can be recognized with higher accuracy than cholecystitis. Cholecystitis can be recognized with higher accuracy on left hand data than right. The findings in this paper partly conform to the theory of TCPD. Though the sample size is relatively small, we could still argue that the methods proposed here are effective and could serve as an assistive tool for TCPD.

Information security is becoming increasingly important in our information driven society. Cryptography is one of the most effective ways to enhance information security. Biometrics based cryptographic key generation techniques, in which biometric features are used to generate cryptographic keys, have been developed to overcome the shortages of the traditional cryptographic methods. An essential issue of biometric cryptographic key generation is to remove the variance between biometric templates of genuine users. In previous works, error correction techniques are used to eliminate these variances. However, these techniques can only be used to remove errors in Hamming metric whereas many biometric templates are real valued vectors and cannot use Hamming distance to measure the similarity, which means that the error correction techniques can not be directly used to remove the variance between these biometric templates. In this paper, we proposed a novel biometric cryptographic framework based on city block distance. In the proposed framework, the real valued biometric feature vector is firstly quantized and then encoded into a binary string in such way that the city block distance between two feature vectors is converted to Hamming distance between two binary strings. After that, the error correction techniques are used to eliminate the errors between the strings of the genuine users. Finally, the error free string is hashed to form a cryptographic key. The experimental results conducted on face and palmprint biometrics demonstrate the effectiveness of the proposed framework.

Characteristics of tongue pose the most important information for tongue diagnosis. So far, extensive studies have been made on extracting tongue surface features. Meanwhile, the sublingual vein diagnosis, one important part of tongue diagnosis, has received increasing attention. In this paper, a novel image acquisition device specially designed for capturing sublingual vein images is introduced. Different from existing tongue image acquisition devices, monochrome industrial CCD with enhanced near infrared sensitivity is used under near infrared light source. Corresponding segmentation method of sublingual veins for the captured near infrared sublingual images is proposed subsequently. Experimental results reveal that the proposed method did indeed segment the sublingual veins from the near infrared sublingual images with an acceptable degree of accuracy.

Doppler ultrasound blood flow signal (DUBFS) is a non-stationary signal that is widely used in the study of the clinical diagnosis of cardiovascular diseases. According to the theory of pulse diagnosis of traditional Chinese medicine, in this paper, a feature extraction method based on Hilbert_Huang transform is proposed in order to investigate the relationship between the DUBFS of wrist radial artery and the pathological changes of certain organs. The extracted features have applied in classification experiments on 4 groups of data, which are healthy persons, gastritis patients, cholecystitis patients and nephritis patients, respectively. Experimental results with high recognition rates demonstrate that Hilbert_Huang transform is an effective method of time-frequency analysis for DUBFS, and extracted features by proposed method have promising discriminating ability between the healthy people and 3 kinds of patients.

Biometric cryptography is a technique using biometric features to encrypt data, which can improve the security of the encrypted data and overcome the shortcomings of the traditional cryptography. This paper proposes a novel biometric cryptosystem based on palmprint features. In this system, the palmprint features, called DoG code, are extracted using Gaussian derivative filters. Then the Reed-Solomon error correcting technique and the logical XOR operation are employed to encrypt and decrypt the data. Experimental results show that this system can obtain a high security with a low false rejection rate.

Palmprint recognition, as one of the most promising biometrics, has received considerable recent biometric research interest. Among various palmprint recognition techniques, coding based methods have been very successful since of its simplicity, high precision, small size of feature and rapidness for both feature extraction and matching. Several filters, such as Gabor and Gaussian, and coding schemes, such as competitive and ordinal measure, have been proposed for palmprint verification and identification. In this paper, we evaluate three filters, Gabor, Gaussian, and the second derivative of Gaussian filter, and two coding schemes, competitive code and ordinal measure on PolyU palmprint database. Results of verification experiment show that Gabor filter and competitive coding scheme is superior to other methods.

Coding based methods are among the most promising palmprint recognition methods. As one representative coding method, the competitive code first convolves the palmprint image with a bank of Gabor filters with different orientations and then encodes the dominant orientation into its bitwise representation. Despite its effectiveness, few investigations have been given to study the influence of the number of filters and the orientation of each filter. In this paper, based on the statistical orientation distribution and the orientation separation principle, we propose a modified fuzzy C-means cluster algorithm to determine the orientations of filters. Experimental results indicate that, the proposed method achieves higher verification accuracy while compared with that of the original competitive code and several state-of-the-art methods. Considering both the computational complexity and the verification accuracy, six filters would be the optimal choice for proposed method.

Coding-based method, which encodes the responses of a bank of filters into bitwise features, has been very successful in palmprint representation and matching. Palmprints, however, are typically multiscale features, where the palm lines can be represented at a higher scale while the wrinkles at a lower scale. In this work, we present a mutliscale competitive code method for efficient palmprint representation and matching. In filterbank design, we adopt the log-Gabor wavelets since of its less overlapping in the frequency domain. In palmprint representation, competitive code is used to encoding the dominant orientation of the filter responses in each scale. In palmprint matching, a fusion rule is proposed to combine the distances obtained using different scales. Experimental results indicate that the proposed method achieves better recognition accuracy and faster matching speed while compared with several state-of-the-art methods.

Palm medicine is an important part of Tradition Chinese Medicine (TCM), which has been widely practiced in China and southeast country of Asia. Palmprint is composed of many lines and some special structures which imply a number of diseases. In this paper a fuzzy approach is proposed to recognize one of special structures in palmprint which is a key process in automated palm diagnosis system. Firstly, a palm image is preprocessed and all palmprint lines are extracted. Secondly, the extracted palm-lines are transformed to an undirected graph according to the connection of the points on the palm-lines. Thirdly, three features are extracted from this graph and their membership functions are defined. Finally, these three features are utilized to recognize one special structure which is called mi structure. Applying our approach to 200 palmprint images, the experimental results are encouraging.

This paper compares the radial artery pulses of 105 young graduate students. The radial artery pulses after performing progressive ergometer for five minutes are different from those at rest. All the pulses become floating and fast. The contours of pulses have three kinds of variability. The incisures of 39 subjects become especially low; sometimes the incisures are lower than the onset of pulse waveform. The tidal waves and dicrotic waves of 32 subjects become higher. The pulses of 34 subjects become smooth. Their incisures and dicrotic waves become lower. These changes can instruct the exercise and training of the young students and athletes

The palm print is a new and emerging biometric feature for personal recognition. The stable line features or "palm lines", which are comprised of principal lines and wrinkles, can be used to clearly describe a palm print and can be extracted in low-resolution images. This paper presents a novel approach to palm line extraction and matching for use in personal authentication. To extract palm lines, a set of directional line detectors is devised, and then these detectors are used to extract these lines in different directions. To avoid losing the details of the palm line structure, these irregular lines are represented using their chain code. To match palm lines, a matching score is defined between two palm prints according to the points of their palm lines. The experimental results show that the proposed approach can effectively discriminate between palm prints even when the palm prints are dirty. The storage and speed of the proposed approach can satisfy the requirements of a real-time biometric system

Principal component analysis (PCA) has been very successful in image recognition. Recent research on PCA-based methods has mainly concentrated on two issues, namely: 1) feature extraction and 2) classification. This paper proposes to deal with these two issues simultaneously by using bidirectional PCA (BD-PCA) supplemented with an assembled matrix distance (AMD) metric. For feature extraction, BD-PCA is proposed, which can be used for image feature extraction by reducing the dimensionality in both column and row directions. For classification, an AMD metric is presented to calculate the distance between two feature matrices and then the nearest neighbor and nearest feature line classifiers are used for image recognition. The results of the experiments show the efficiency of BD-PCA with AMD metric in image recognition.

Appearance-based methods, especially linear discriminant analysis (LDA), have been very successful in facial feature extraction, but the recognition performance of LDA is often degraded by the so-called "small sample size" (SSS) problem. One popular solution to the SSS problem is principal component analysis (PCA) + LDA (Fisherfaces), but the LDA in other low-dimensional subspaces may be more effective. In this correspondence, we proposed a novel fast feature extraction technique, bidirectional PCA (BDPCA) plus LDA (BDPCA + LDA), which performs an LDA in the BDPCA subspace. Two face databases, the ORL and the Facial Recognition Technology (FERET) databases, are used to evaluate BDPCA + LDA. Experimental results show that BDPCA + LDA needs less computational and memory requirements and has a higher recognition accuracy than PCA + LDA.

Characteristics of tongue pose the most important information for traditional Chinese medicine diagnosis. So far, extensive studies have been made on extracting tongue surface features, but rarely refer to sublingual vein that is also diagnostically important. This paper presents a novel approach to extract spatial characteristics of sublingual vein based on the HSI color space using the H and S components. Sublingual vein structures have been successfully mapped for 113 out of 150 patients and healthy subjects.

Palm lines, which consist of principal lines and wrinkles, are stable and essential traits for palmprint-based individual identification and can be extracted in low-resolution images. However, the research on palm-line detection has done little. Due to special properties of palmprint, in addition to the structure feature, width of the palm-line, which generally reflects strength information, is important to identify palms especially when various palmprints have similar structures. In this paper, a palm-line detection approach is proposed to simultaneously extract structure and strength features of palm lines by minimizing a local image area which is of similar brightness to each individual pixel. The presented method has been tested on the PolyU palmprint database and compared with the canny edge detector and SUSAN edge finder. Experimental results illustrate the effectiveness of this approach.

This paper presents a novel approach of palmprint authentication based on the fusion of the phase and orientation information. This approach is an improvement of a previous palmprint recognition method - fusioncode method (A. Kong and D. Zhang, 2004). In the proposed approach, the phase information (fusion-code) of a palmprint is extracted by using four 2-D Gabor filters with different orientations, and at the same time, the orientation information (called orientationcode) of the palmprint is also extracted. The fusioncode and the orientationcode are fused to make a new feature, called the palmprint phase orientation code (PPOC). At the matching stage, a modified Hamming distance is defined to measure the similarity of two PPOCs. This approach is tested on a palmprint database containing 7605 samples and the experimental results show that the PPOC approach greatly improves the performance of the fusioncode method.

This paper proposes a novel algorithm for the automatic coarse classification of iris images using a box-counting method to estimate the fractal dimensions of the iris. First, the iris image is segmented into sixteen blocks, eight belonging to an upper group and eight to a lower group. We then calculate the fractal dimension value of these image blocks and take the mean value of the fractal dimension as the upper and the lower group fractal dimensions. Finally all the iris images are classified into four categories in accordance with the upper and the lower group fractal dimensions. This classification method has been tested and evaluated on 872 iris cases, and the proportions of these categories in our database are 5.50%, 38.54%, 21.79% and 34.17%. The iris images are classified with the double threshold algorithm, which classifies iris images with an accuracy of 94.61%. When we allow for the border effect, the double threshold algorithm is 98.28% accurate.

Principal component analysis (PCA) has been very successful in image recognition. Recent researches on PCA-based methods are mainly concentrated on two issues, feature extraction and classification. In this paper we propose bi-directional PCA (BDPCA) with assembled matrix distance (AMD) metric to simultaneously deal with these two issues. For feature extraction, we propose a BDPCA approach which can reduce the dimension of the original image matrix in both column and row directions. For classification, we present an AMD metric to calculate the distance between two feature matrices. The results of our experiments show that, BDPCA with AMD metric is very effective in image recognition.

Automated tongue image segmentation, in Chinese medicine, is difficult due to two special factors: 1) there are many pathological details on the surface of the tongue, which have a large influence on edge extraction; 2) the shapes of the tongue bodies captured from various persons (with different diseases) are quite different, so they are impossible to describe properly using a predefined deformable template. To address these problems, in this paper, we propose an original technique that is based on a combination of a bi-elliptical deformable template (BEDT) and an active contour model, namely the bi-elliptical deformable contour (BEDC). The BEDT captures gross shape features by using the steepest decent method on its energy function in the parameter space. The BEDC is derived from the BEDT by substituting template forces for classical internal forces, and can deform to fit local details. Our algorithm features fully automatic interpretation of tongue images and a consistent combination of global and local controls via the template force. We apply the BEDC to a large set of clinical tongue images and present experimental results.

Palm-lines, including the principal lines and wrinkles, can describe a palmprint clearly. This paper presents a novel approach of palm-line extraction for the online palmprints. This approach is composed of two stages: coarse-level extraction stage and fine-level extraction stage. In the first stage, morphological operations are used to extract palm-lines in different directions. In the second stage, for each extracted line, a recursive process is devised to further extract and trace the palm-line using the local information of the extracted part. Experimental results show that the proposed approach is suitable for palm-line extraction.

Tongue diagnosis is an important diagnosis method in traditional Chinese medicine (TCM) and recently the development of automated tongue image analysis technology has been carried out. Automated tongue segmentation is difficult due to the complexity of pathological tongue, variance of tongue shape and interference of the lips. In this paper, we present a novel method for automated tongue segmentation by combining polar edge detector and active contour model. First, a novel polar edge detector is proposed to effectively extract the edge of the tongue body. We then introduce a method to filter out the edge that is useless for tongue segmentation. A local adaptive edge bi-thresholding technique is also proposed. Finally, an initialization and active contour model are proposed to segment the tongue body from the image. Experimental results demonstrate that the novel tongue segmentation can segment the tongue accurately. A quantitative evaluation on 50 images indicates that the mean DCP (the distance to the closest point) of the proposed method is 5,86 pixels, and the average true positive (TP) percent is 97.2%.

The dynamical analysis of pulse variability gives new insight into researches of cardiovascular system's dynamics. Firstly, long-term pulse variability analysis for the researches on cardiovascular system was proposed. Secondly, approximate entropy was applied to analyze three groups of long-term pulse waveform variabilities and we found that the pulses' approximate entropies of patients with cardiovascular disease preferred to smaller value and less irregularity. What more, the pulse variability of the patient with pacemaker newly implanted was also studied. Finally, the pulse variability's clinical value for cardiovascular system was concluded.