Inverse problems in medical imaging, such as undersampled magnetic resonance imaging (MRI) and sparse-view computed tomography (CT) reconstruction, are essential yet challenging tasks for achieving accurate and reliable diagnostic images. Traditional reconstruction approaches, including iterative optimization algorithms and supervised deep learning methods, often struggle with limited adaptability across imaging protocols, substantial computational requirements, and poor generalization between different imaging modalities. Diffusion-based generative models have recently demonstrated promising results; however, these methods frequently suffer from cumulative estimation errors in their sampling processes, limiting their practical performance and robustness. In this paper, we propose a novel framework called Parallel Trajectory Constrained Sampling (PCS), which substantially enhances image reconstruction quality by explicitly enforcing consistency with the underlying physical measurement process. Specifically, PCS introduces a measurement-domain diffusion model whose reverse stochastic differential equation (SDE) trajectory is analytically determinable, thus obviating the need for a learned score estimator within the measurement domain. Furthermore, a parallel trajectory constraint is formulated to rigorously align the reverse sampling paths of the measurement and image diffusion processes, ensuring strict adherence to the known physical model at every sampling step. The proposed PCS method is flexible and can seamlessly integrate various SDE-based diffusion priors. Extensive experiments on representative inverse problems—including undersampled MRI reconstruction, sparse-view CT reconstruction, and image super-resolution—demonstrate that PCS consistently outperforms existing state-of-the-art diffusion-based reconstruction methods. Although current evaluations focus specifically on MRI and CT modalities, the PCS framework holds considerable promise for broader applicability to other imaging modalities and inverse problems, which we plan to investigate in future studies.

Chest X-Ray Vision-Language pretraining (VLP) leverages large-scale radiograph-report pairs to develop joint image-text representations, demonstrating significant potential for medical image diagnosis. However, existing VLP approaches often overlook the multi-view nature of chest X-Rays, and some multi-view methods apply uniform feature fusion, neglecting view-key semantic contributions. Moreover, random cross-modal Masked Language Modeling (MLM) fails to facilitate effective interactions, impeding representation alignment. Additionally, global alignment in VLP may lead to the false-negative problem. To address these limitations, we propose a novel medical VLP framework comprising three core components. First, a Key Semantics-enhanced Multi-view MLM module aggregates pathology-relevant patches across views, providing semantically rich supervision for MLM. A local semantics enhancing approach, which identifies and aggregates pathology-relevant key patches across views to guide MLM. Second, a Frontal-Lateral Alignment module extracts view-specific pathological features, ensuring semantic consistency and preserving critical information during aggregation. This module independently extracts pathological features from both views to preserve view-specific information while ensuring semantic consistency, which mitigates the loss of crucial information during aggregation. Third, a High-order Semantic Alignment approach mitigates false-negative issues by aligning features with semantically consistent clusters, enhancing global alignment through prototype-level semantics. Extensive experiments across seven public datasets demonstrate that our framework outperforms state-of-the-art methods in four downstream tasks, validating its efficacy. The code is available at https://github.com/sajiutea/F-L.

Brain age has emerged as a critical biomarker for assessing neurodevelopmental health and aging trajectories, demonstrating significant potential in early detection and monitoring of neurological and psychiatric disorders. While existing brain age prediction models predominantly rely on structural MRI (sMRI) due to their rich anatomical detail and predictive accuracy, resting-state functional MRI (rs-fMRI)—which captures dynamic brain connectivity—remains underutilized despite offering complementary insights. This study proposes a highly accurate and generalizable brain age prediction framework that effectively fuses sMRI and rs-fMRI modalities. Specifically, we integrate a Squeeze-and-Excitation Transformer for structural feature extraction with a Graph Frequency Recurrent Network for modeling functional dynamics. Our hybrid model achieves a MAE of 1.31 years and Pearson's R of 0.976 on the ABIDE I dataset while generalizing effectively across independent datasets, thus demonstrating the utility of multimodal fusion for robust brain age estimation.

Current medical vision-language pre-training models primarily follow two paradigms: report-supervised cross-modal alignment pre-training and reconstruction-based self-supervised pre-training. The former enhances the discriminative power of representations, while the latter facilitates fine-grained representation learning. However, naively combining these two paradigms inherits their inherent limitations: reconstruction-based methods treat all image patches equally during reconstruction, failing to effectively capture critical pathological details-since disease-related regions typically occupy only a small fraction of the image. Meanwhile, alignment-based methods suffer from suboptimal representations due to the presence of false negatives. To address these challenges, we propose a novel pre-training framework that integrates two key components: Pathology-Aware Reconstruction (PAR) and Discriminative Knowledge-Boosted Alignment (DKBA). Through a cascaded training strategy, our framework effectively combines the strengths of both paradigms while mitigating their inherent limitations. During the reconstruction pre-training stage, PAR incorporates pathology-aware priors to enhance the model's ability to capture fine-grained pathological details. In the alignment pre-training stage, DKBA leverages a medical knowledge graph as external supervision to improve cross-modal clustering alignment, thereby reducing the negative impact of false negatives. Extensive experiments on diverse downstream medical imaging tasks including image classification, object detection, and semantic segmentation, demonstrate the superior generalization capabilities of our method. Our code is publicly available at https://github.com/Felix1118/PADKB.

Diffusion models show great potential in solving inverse problems, including MRI reconstruction. With its unique characteristics, medical imaging demands both efficiency and accuracy in the reconstruction process. However, existing MRI reconstruction methods based on diffusion models often fall short of fully leveraging the available measurements during sampling. Consequently, these methods suffer from compromised reconstruction quality and elevated bias, especially when dealing with large acceleration factors. In response to these challenges, we propose Dual Manifold Constraints (DMC), a fast MRI reconstruction method based on diffusion models. We treat the sampling process as a combination of denoising and adding noise processes, and we constrain these two processes using both pristine measurements and their noisy counterparts to adapt to the geometry of diffusion. It’s worth noting that we propose a method to estimate the noisy measurement that satisfies the sub-sampling process to maintain the current data manifold when performing data consistency constraints. Experimental results show that our method outperforms the latest diffusion-based methods regarding both reconstruction speed and accuracy, and exhibits strong out-of-distribution generalization performance.

Since pathology occupies only a small portion of an X-ray, which means that a large portion of the information may be irrelevant to the paired radiology report, the Chest X-rays Report Understanding (CRU) task focuses on how to utilize small regions of the case to improve the performance of medical VLP. However, existing studies have neglected the fine-grained false negative samples of medical visual representations, resulting in their poor performance in CRU scenarios, which we attribute this to the fine-grained feature collapse problem. To address this issue, we propose an intra-modality siamese knowledge generation and inter-modality alignment framework, termed Chest X-rays Report Understanding Framework(CRUF). CRUF leverages the siamese knowledge in image-text pairs as guiding signals to distinguish fine-grained false negative and negative samples within the modality, and further narrows the distance between false negative and positive samples between modalities, accurately aligning the case regions of each image with the corresponding medical terms. Experimental results on multiple downstream medical image datasets covering tasks such as image classification, object detection, and semantic segmentation demonstrate the stability and outstanding performance of our framework. Code is available at https://github.com/cl-red/CRUF.

With the rise of large-scale model applications, leveraging these models as the base network for efficient transfer learning has garnered increasing attention. Currently, parameter-efficient transfer learning methods have made significant improvements in reducing the number of trainable parameters but introduce latency during inference. In this study, we propose an enhanced adaptation of the adapter using a reparameterization technique, revamping the activating layers into linear layers. This modification retains the high-dimensional fine-tuning capability of the adapter for visual tasks while avoiding additional inference latency. We name this plug-and-play module the Re3adapter, which optimizes the model with only 0.26% of the parameters and introduces no inference latency. Experimental results demonstrate its clear advantages in traditional classification and medical tasks.

Point Cloud Registration (PCR) is fundamental for the automatic perception of our space. With the rapid development of deep neural network, the community has swiftly adapted to this data-driven technique, and achieved promising performances. However, most existing learning-based methods attempt to conduct PCR within specific ideal experimental settings, in which the ground truth transformations are accessible and most of the data points have one-to-one correspondences. But in real-world scenarios, the GT transformations are often unknown, and point clouds may only share partially overlapped regions. It leads us to a challenging yet practical issue: How to perform Partial-to-Partial (PtP) Point Cloud Registration without pre-acquired supervisions? In this paper, we aim to tackle both challenges under a unified framework. To achieve this, we propose a novel Focal Anchor Generator to emulate the human perceptual process, particularly focusing on the mutual cloud parts. On top of it, a set of Multi-Consistency constraints are introduced to equip our model with the unsupervised learning ability, which is highly applicable. Extensive experiments have demonstrated the distinctive quality of our proposed framework. We believe this work will broaden the scope of PCR research and enhance the applicative potential of PCR algorithms. (The project code has been released on github.com/chengxiaojin/FGMC-UPCR).

Semi-supervised image semantic segmentation, a vital but challenging task in multimedia applications, aims to accurately classify pixels with limited labeled data. Traditional approaches in this domain often grapple with the confirmation bias problem, where models, influenced by their own predictions, become prone to replicating errors. To address this critical issue, our research introduces a cross-network-crossview consistency learning framework. This novel paradigm significantly reduce the confirmation bias through diversifying the learning perspectives. Integral to our approach are two components: a pseudo-label validation and filtering mechanism, and a cross-contrastive learning module within the feature domain. These elements work in synergy to not only amplify the accuracy of the model but also its robustness against varied data scenarios. Extensive evaluations, conducted across multiple datasets, clearly demonstrate the effectiveness of our method. In comparison to existing state-of-the-art models, our approach exhibits marked improvements, especially in the challenging contexts of semisupervised image semantic segmentation. The code is available at https://github.com/Sstar2orchid/C3T.

Multi-focus image fusion (MFIF) creates an image from different source images with various sensors or optical settings as the devices can’t focus all objects at different distances. Most of the MFIF methods have several limitations in encoder enough features from the images and the result are not robust. To overcome the primary issue, we present a robust fusion algorithm based on the Frequency mask and the Hyperdimensional computing. We propose the Frequency Mask Filter (FMF) to get the narrow-band signals by encoding the frequency domain vector through the mask filter in the frequency domain. The Hyperdimensional encoder uses monogenic mapping, in which the multi-modulation features (MMF) such as the frequency, phase and amplitude are dynamically selected to obtain robust focus maps. Generated by multiscale monogenic representations of each image, the narrow-band image are mapped to hypervector encoding. Hyperdimensional encoder shows the energetic and structural information and leads to robust fusion results. Our proposed method is far superior to the existing MFIF method in terms of both objective evaluation metrics and visual effects on three publicly available datasets.Additionally, our proposed method requires only 0.88 seconds and has a parameter count of 0.13 million for multi-focus image fusion.

Medical image segmentation is a critical task in the field of medical image analysis. Recently, there has been increasing attention on scribble-supervised medical image segmentation due to its simplicity for label generation. However, the performance of a scribble-based task highly relies on the quality of learning from inconsistent annotations and the classification of pixels in high-entropy regions. In this paper, we propose a novel framework called SRPA that combines both ScribbleMatch and Reliable-Guided Pixel Alignment to enhance the performance of the scribble-based task. The ScribbleMatch technique utilizes the pseudo label incorporated from two different weakly perturbed views of the same image to supervise a strongly perturbed view, which assists in boosting the quality of the shape information learning as the scribble-based task lacks the prototypes to consistently capture shape prior to model training. The Reliable-Guided Pixel Alignment technique employs reliable pixels selected by contrasting two weakly perturbed views, which serve as the standard for blurred pixels in strongly perturbed images to maximally align. This ensures the reliable classification of high-entropy pixels. Our method is evaluated on the public ACDC and MSCMRseg datasets, and the results demonstrate that our approach surpasses current scribble-supervised segmentation methods. Code will be available at https://github.com/RheinSXY/SRPA.

Deformable image registration is a basic building block in intelligent bioinformatical analysis and biomedicine systems. With the rapid development of deep learning, the community has witnessed a great leap via this effective data-driven technique. Recently, the Vision Transformer, famous by its long-range modeling ability, has been successfully used in the field of medical image registration. However, the existing ViT based techniques are deemed to have certain limitations. Firstly, these methods often transplanted the transformer module directly into the networks, while did not dive deeper to explore its compatibility to practical registration tasks. Moreover, self-attention’s relatively rigid all-to-all patching strategy may cause undesirable discontinuity effect to the spatial calculation. To address these issues, we propose a novel medical image registration framework based on an efficient image prior learning and fusion mechanism. Unlike the existing prior-based registration methods, our model is capable of learning task-specific saliency priors, without the need of hand-crafted features, or heavy-loaded auxiliary tasks, or pre-acquired expensive annotations. Then, followed by a multi-scale patch embedding module, the self-driven saliency prior is integrated into a ViT block with an active feature fusion mechanism, to further expand our network’s structural learning abilities. Extensive experiments on multiple data sets have demonstrated the superior quality of the proposed framework. We believe this plug-and-play model will bring about more application potentials to the community (Project webpage: https://github.com/raincity212/SPF-Net).

At present, deep learning based heart sound diagnosis algorithms are mostly complex and large models for high accuracy, which are difficult to deploy on mobile devices due to the high number of parameters and large computational cost. The current mainstream approach for processing heart sound signals involves utilizing their Mel-frequency cepstral coefficients (MFCC) features. However, most existing methods have overlooked the multi-channel characteristics of MFCC. To address this issue, we propose a Quaternion Dynamic Representation with Joint Learning (QDRJL) neural network for learning MFCC multi-channel features. Our proposed approach combines quaternion dynamic convolution with dynamic weighting and the Quaternion Interior Learning Block (QILB). Finally, we present a global and energy joint learning branch for jointly learning MFCC features. The success of the proposed quaternion network depends on its ability to utilize the internal relations between quaternion-valued input features and the definition of the dynamic weight variables in the augmented quaternion domain. We assessed various state-of-the-art classification algorithms for detecting heart sounds and found that our proposed classifier achieved an accuracy of up to 97.2%, outperforming existing models. Our experimental evaluation, using the 2016 PhysioNet/CinC Challenge dataset, revealed that our model could reduce the number of network parameters to 25% due to quaternion properties.

Fetal heart rate (fHR) is critical for assessing fetal health and diagnosing disorders, such as fetal distress, congenital heart disease, and intrauterine growth retardation. With the rapid development of the Internet of Medical Things (IoMT), fetal R-peak detection plays an important role in diagnosing heart defects during pregnancy. However, due to the nonlinear mixing of multiple sources in the noninvasive signals and the low signal-to-noise ratio (SNR), it is difficult to obtain accurate R-peak detection result. This article presents a dual self-calibrating system based on a spectral attention kernel independent component analysis (SA-KICA) module and a self-calibrating fetal R-peak detection (SC-FRD) module. SA-KICA is an ICA-based calibration module constructed by the spectral attention mechanism, which was sought from short-time Fourier transform (STFT) and was shipped back to original signal with convolution to achieve perfect maternal electrocardiogram (MECG) separation in high-dimensional linear separable space. Then, a periodic and morphological-based channel selector is designed to select the optimal MECG. After MECG removal, to further improve the performance of fetal R-peak detection, the SC-FRD module is introduced to utilize the interior peak information and self-calibrating strategy, which includes variance-based fetal R-peak seed selection, time-varying coarse prediction, and adaptive probability mask calibration. The proposed framework is a primary attempt to concurrently introduce the nonlinear feature, spectral information, and self-calibrating strategy in the field of fetal ECG processing. The framework achieved excellent performance in fetal R-peak detection accuracy on a simulated data set and two public data sets with varying divergence and richness of resources. The experimental results show that our framework is superior to existing methods and can be used as a potential fetal monitoring method in the application of IoMT. The code is released inhttps://github.com/bfyjr/NI-FECG-Extraction.

In recent years, auxiliary diagnosis technology for cardiovascular disease based on abnormal heart sound detection has become a research hotspot. Heart sound signals are promising in the preliminary diagnosis of cardiovascular diseases. Previous studies have focused on capturing the local characteristics of heart sounds. In this paper, we investigate a method for mapping heart sound signals with complex patterns to fixed-length feature embedding called HS-Vectors for abnormal heart sound detection. To get the full embedding of the complex heart sound, HS-Vectors are obtained through the Time-Compressed and Frequency-Expanded Time-Delay Neural Network(TCFE-TDNN) and the Dynamic Masked-Attention (DMA) module. HS-Vectors extract and utilize the global and critical heart sound characteristics by masking out irreverent information. Based on the TCFE-TDNN module, the heart sound signal within a certain time is projected into fixed-length embedding. Then, with a learnable mask attention matrix, DMA stats pooling aggregates multi-scale hidden features from different TCFE-TDNN layers and masks out irrelevant frame-level features. Experimental evaluations are performed on a 10-fold cross-validation task using the 2016 PhysioNet/CinC Challenge dataset and the new publicly available pediatric heart sound dataset we collected. Experimental results demonstrate that the proposed method excels the state-of-the-art models in abnormality detection.

Due to the continuous growing complexity of manufacturing information (MI) and increasing need to exchange this information among various software applications, the unified fundamental manufacturing process data are necessary. This paper put forwards a MI model whose core contents are products, processes, resources and plants, meanwhile, their complex relations are also explained. Aiming to specify the concepts' unambiguous definition and their relationships both in syntax and semantics, this research proposes an ontology modeling approach and constructs the MI model in Ontology Web Language (OWL).Besides, the detailed characteristics of three typical semantic retrieval and visualization plug-ins in protégé are summarized, so as to realize a better understanding of the complex ontology model. In order to share and reuse this model in collaborative working environment, a semantic similarity algorithm is proposed and the procedures of realizing semantic retrieval is discussed, then a prototype system of semantic retrieval in web browser is developed. In the end, an example is given to demonstrate and verify the above work.