Longitudinal deformable registration is crucial for precision radiotherapy but is challenged by large-scale organ deformations and anatomical coupling. While existing deep learning registration methods have shown promise, they often face two key limitations: (1) a trade-off in feature extraction between local details and long-range dependencies, and (2) feature distortion from the naive fusion of spatially misaligned features. To overcome these challenges, we propose HCFMorph, a novel, unified coarse-to-fine registration framework. Our framework introduces three key innovations: (1) a Synergistic Mamba-Conv module to model coupled organ motion by capturing global-local features; (2) a Contribution-Aware Feature Fusion module to adaptively fuse features from the fixed and moving images, thereby mitigating feature misalignment; and (3) a Deformation-Guided Hierarchical Refinement strategy in the decoder, which addresses large-scale deformations by recovering the field in a coarse-to-fine manner and leverages low-resolution fields to guide and pre-align high-resolution features before the CAFF stage, forming a novel feedback loop. Experiments on a clinical dataset of 432 longitudinal CT scans from 97 cervical cancer patients show that HCFMorph significantly outperforms state-of-the-art methods. It achieves dominant anatomical accuracy while maintaining excellent topological integrity with a near-zero Jacobian folding rate, striking a superior balance between accuracy and plausibility.

Spatial transcriptomics (ST) provides groundbreaking opportunities to study biological processes and disease mechanisms by measuring gene expression profiles and spatial coordinates. Accurate spatial domain identification via effective data representation is crucial for biological discovery. We propose a multi-context modeling framework with spatial adaptive enhancement (SAE) for ST data analysis. The SAE algorithm leverages spatial information to denoise and enhance expression data, eliminating the adverse impacts of high noise and sparsity inherent in ST data. Given the enhanced data, we incorporate diverse sample context relationships, including spatial, expression, and random non-neighbor contexts, via masked attention. This multi-context strategy overcomes the reliance of current methods on local neighbors, further enhancing the expressiveness of sample embeddings. A graph Laplacian loss preserves sample adjacency in latent space, ensuring spatial coherence of the identified domains. Experiments on five public datasets demonstrate that our method outperforms seven state-of-the-art benchmarks in domain identification and robustness. As a plug-and-play algorithm, SAE significantly improves benchmarks' performance, highlighting its broad application potential.

Single-cell multi-omics technologies have revolutionized the study of cell states and functions by simultaneously profiling multiple molecular layers within individual cells. However, existing methods for integrating these data struggle to preserve critical feature information and fail to exploit known regulatory knowledge, which is essential for understanding cell functions. This limitation hinders their ability to provide comprehensive and accurate insights into cells. Here, we propose FactVAE, an innovative factorized variational autoencoder designed for the robust and accurate understanding of single-cell multi-omics data. FactVAE integrates the factorization principle into the variational autoencoder framework, ensuring the preservation of feature information while leveraging the non-linear capture of sample information by neural networks. Additionally, known regulatory knowledge is incorporated during model training, and a knowledge transfer strategy is employed for cell embedding optimization and data augmentation. Comparative analyses of single-cell multi-omics datasets from different protocols and the spatial multi-omics dataset demonstrate that FactVAE not only outperforms benchmark methods in clustering performance but also generates augmented data that reveals the clearest cell-type-specific motif expression. Moreover, the feature embeddings captured by FactVAE enable the inference of potential and reliable gene regulatory relationships. Overall, FactVAE's superior performance and strong scalability make it a promising new solution for single-cell multi-omics data analysis.

High-speed and good trajectory controllability are two critical attributes of small artificial aquatic surface robots. Inspired by the moving mechanism of water striders, we herein propose insect-scale soft aquatic surface robots utilizing piezoelectric actuation coupled with asymmetric footpads. The aquatic surface robots move quickly without penetrating the water-air interface and utilize incoordinate propulsive force from asymmetric footpads to realize precise trajectory control. An ultrafast linear speed of 21.82 BL/s (24 cm/s) and a high angular speed of 303 °/s are achieved, which are advanced among small aquatic surface robots. We showcase agility and maneuverability by navigating through a water maze with a total route length of 88 cm in an actual driving time of 16.5 s. Moreover, proof-of-concept for search and rescue operations is demonstrated by using a robot to tow an on-water monitoring system to record a real-time video showing the “SOS” symbol. An untethered robot is also demonstrated to improve the practical potential. The design principles, operation mechanisms, and steering characteristics presented in this work provide fundamental guidelines for the development of future small aquatic surface robots.

Bias fields adversely affect various automatic analysis technologies. Therefore, bias field correction is essential. However, deep learning based methods encounter challenges in obtaining ground truth. Although existing methods attempt to address this problem by using training-free techniques or constructing datasets with approximations of the ground truth, the lack of task-oriented guidance, training instability, and inappropriate use of approximations still impact performance. Additionally, different approaches for bias field correction, i.e., estimating bias fields versus directly restoring clean images, exhibit different performances. However, there is no consensus on the best way, resulting in limited performance in some cases. To address these problems, we propose a bias field generation method to construct the dataset and provide task-oriented information. We then propose the concept of Equivalent of Residual Mapping (ERM) to analyze the advantages of estimating bias fields. According to ERM, we propose the Bias field Estimation Convolutional Neural Network (BECNN). Finally, we propose the Dual Route Implicit Supervised Learning (DRISL) strategy to balance the guidance derived from approximations of the ground truth with over-dependence on them. The proposed method is compared qualitatively and quantitatively with the correlated methods. Experiment results demonstrate that the proposed method performs effectively both on bias field estimation and correction.

Dimensionality reduction and clustering are crucial tasks in single-cell RNA sequencing (scRNA-seq) data analysis, treated independently in the current process, hindering their mutual benefits. The latest methods jointly optimize these tasks through deep clustering. However, contrastive learning, with powerful representation capability, can bridge the gap that common deep clustering methods face, which requires pre-defined cluster centers. Therefore, a dual-level contrastive clustering method with nonuniform sampling (nsDCC) is proposed for scRNA-seq data analysis. Dual-level contrastive clustering, which combines instance-level contrast and cluster-level contrast, jointly optimizes dimensionality reduction and clustering. Multi-positive contrastive learning and unit matrix constraint are introduced in instance- and cluster-level contrast, respectively. Furthermore, the attention mechanism is introduced to capture inter-cellular information, which is beneficial for clustering. The nsDCC focuses on important samples at category boundaries and in minority categories by the proposed nearest boundary sparsest density weight assignment algorithm, making it capable of capturing comprehensive characteristics against imbalanced datasets. Experimental results show that nsDCC outperforms the six other state-of-the-art methods on both real and simulated scRNA-seq data, validating its performance on dimensionality reduction and clustering of scRNA-seq data, especially for imbalanced data. Simulation experiments demonstrate that nsDCC is insensitive to "dropout events" in scRNA-seq. Finally, cluster differential expressed gene analysis confirms the meaningfulness of results from nsDCC. In summary, nsDCC is a new way of analyzing and understanding scRNA-seq data.

Regularization-based continuous learning (RCL) has been proven to be highly effective on struggling against catastrophic forgetting. However, its application in medical image segmentation (MIS) is relatively scarce. In this paper, we provide a unified description of 6 RCL methods (LwF, LwM, EWC, SI, MAS, and PIGWM) using Taylor expansion and investigate their performances in 2 classic MIS scenarios, namely retinal vessel segmentation (RVS) and cardiac left ventricle segmentation (CLVS) on 8 datasets (CHASE, DRHAGIS, RITE and STARE for the former and M&Ms, LVSC, ACDC and SCD for the latter). We also explore the influence of different task orders (easy to hard or hard to easy), optimizers (Adam or SGD), and parameter capacities (2, 3, 4 or 5 down- and up-sampling pairs) on the performance of these methods. Our experimental results show that these methods are capable of mitigating catastrophic forgetting to a certain extent. Comparing to a hard-to-easy order, most of the methods perform better on all of the already known tasks in an easy-to-hard order. Optimizer Adam performs better on RVS and CLVS. Capacity increases are obviously effective for CLVS, but they have no significant impact on RVS.

BACKGROUND: The discovery of critical biomarkers is significant for clinical diagnosis, drug research and development. Researchers usually obtain biomarkers from microarray data, which comes from the dimensional curse. Feature selection in machine learning is usually used to solve this problem. However, most methods do not fully consider feature dependence, especially the real pathway relationship of genes. RESULTS: Experimental results show that the proposed method is superior to classical algorithms and advanced methods in feature number and accuracy, and the selected features have more significance. METHOD: This paper proposes a feature selection method based on a graph neural network. The proposed method uses the actual dependencies between features and the Pearson correlation coefficient to construct graph-structured data. The information dissemination and aggregation operations based on graph neural network are applied to fuse node information on graph structured data. The redundant features are clustered by the spectral clustering method. Then, the feature ranking aggregation model using eight feature evaluation methods acts on each clustering sub-cluster for different feature selection. CONCLUSION: The proposed method can effectively remove redundant features. The algorithm's output has high stability and classification accuracy, which can potentially select potential biomarkers.

MRI brain segmentation plays an important part in computer-aided diagnosis, which visually reveals the changes in brain structure for doctors to quickly and accurately discover and treat diseases related to brain tissue morphology. The fuzzy C-means (FCM) algorithm performs well when the segmenting images with no noise and with intensity uniformity. However, the MRI brain images are always defective in noise and intensity nonuniformity and thus we propose a novel FCM algorithm named adaptive FCM with neighborhood membership (FCM_anm). We design a filtering process with neighborhood membership to reduce the negative influence of noise and a novel objective function which further considers the spatial membership information adaptively. Finally, to verify the performance of our method, several experiments comparing among the Experimental results demonstrate the proposed method consistently outperforms the state-of-the-art FCM-based algorithms in synthetic images, simulated and real brain MR images with effects of the noise and intensity non-uniformity.

Diabetic retinopathy (DR) is one of the most concerning, common and serious diseases in the ophthalmology community. Early detection and treatment of DR can significantly reduce the risk of vision loss in patients. Traditional DR automatic classification algorithms rely on the precise detection of microaneurysms (MA) and hemorrhage (H) lesions. Such lesion annotation is an expensive and time-consuming process, hence it is expected to develop automatic grading methods with only image-level annotations. The lack of the position of MA and H hinders the traditional supervised algorithms for the accurate identification. In our work, we formulate the weakly supervised DR grading as a multi-instance learning problem, and propose a domain adaptation multi-instance learning with attention mechanism for DR grading. Specifically, labeled instances are generated by cross-domain to filter irrelevant instances in the target domain. To model the relationship between the suspicious instances and bag label, a multi-instance learning with attention mechanism is developed to acquire the location information of highly suspected lesions and predict the grade of DR. We evaluate our proposed algorithm on the Messidor dataset, and the experimental results demonstrate that it achieves an average accuracy of 0.764 and an AUC value of 0.749 respectively, outperforming state-of-the-art approaches.

Previous work has shown that machine learning algorithms lend themselves to clinical decision-making and are a valuable tool for physicians. For clinical data, it is often necessary to assign multiple labels to a patient record by choosing from a large number of potential labels. A key problem in learning from multi-labelled data is how to exploit the information contained in the correlations between labels. The hypergraph-based multi-label learning method learns from data by exploiting the spectral property of the hypergraph that encodes the correlation structure of labels. However, the problem with this method is the difficulty with which interpretations can be made. This is mainly due to its inability to recognize the importance of key features in the original feature space. Moreover, it is hard to comprehensively capture the complex structure of the correlations between labels. To overcome these difficulties and improve interpretability, we propose an l21-norm regularized Graph Laplacian multi-label learning to perform feature selection and label embedding simultaneously. In-depth experimental studies, using the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database, validate the effectiveness of our approach.

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Predicting cognitive performance of subjects from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Multi-task based feature learning (MTFL) have been widely studied to select a discriminative feature subset from MRI features, and improve the performance by incorporating inherent correlations among multiple clinical cognitive measures. It is known that the brain imaging measures are often correlated with each other, and AD is closely related to the inter-correlation among different brain regions. However, the multi-task based feature learning (MTFL) method neglects the inherent correlation among brain imaging measures. We present a novel regularized multi-task learning approach via a joint sparsity-inducing regularization to effectively incorporate both a relatedness among multiple cognitive score prediction tasks and a useful inherent correlation between brain imaging measures by exploiting correlations among features. It allows the simultaneous selection of a common set of biomarkers for all tasks and the preservation of the inherent structure of imaging measures. The reported experiments on the ADNI dataset show that the proposed method is effective and promising.

Alzheimer’s disease (AD), the most common type of dementia, not only imposes a huge financial burden on the health care system, but also a psychological and emotional burden on patients and their families. There is thus an urgent need to infer trajectories of cognitive performance over time and identify biomarkers predictive of the progression. In this article, we propose the multi-task learning with fused Laplacian sparse group lasso model, which can identify biomarkers closely related to cognitive measures due to its sparsity-inducing property, and model the disease progression with a general weighted (undirected) dependency graphs among the tasks. An efficient alternative directions method of multipliers based optimization algorithm is derived to solve the proposed non-smooth objective formulation. The effectiveness of the proposed model is demonstrated by its superior prediction performance over multiple state-of-the-art methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are consistent with prior medical studies.

Class imbalance is one of the challenging problems for machine learning in many real-world applications. Other issues, such as within-class imbalance and high dimensionality, can exacerbate the problem. We propose a method HPS-DRS that combines two i

Many lung nodule computer-aided detection methods have been proposed to help radiologists in their decision making. Because high sensitivity is essential in the candidate identification stage, there are countless false positives produced by the initial suspect nodule generation process, giving more work to radiologists. The difficulty of false positive reduction lies in the variation of the appearances of the potential nodules, and the imbalance distribution between the amount of nodule and non-nodule candidates in the dataset. To solve these challenges, we extend the random subspace method to a novel Cost Sensitive Adaptive Random Subspace ensemble (CSARS), so as to increase the diversity among the components and overcome imbalanced data classification. Experimental results show the effectiveness of the proposed method in terms of G-mean and AUC in comparison with commonly used methods.

The performance of traditional classification algorithms can be limited on imbalanced datasets. In recent years, the imbalanced data learning problem has drawn significant interest. In this work, we focus on designing modifications to neural network, in order to appropriately tackle the problem of multiclass imbalance. We propose a hybrid method that combines two ideas: diverse random subspace ensemble learning with evolutionary search, to improve the performance of neural network on multiclass imbalanced data. An evolutionary search technique is utilized to optimize the misclassification cost under the guidance of imbalanced data measures. Moreover, the diverse random subspace ensemble employs the minimum overlapping mechanism to provide diversity so as to improve the performance of the learning and optimization of neural network. We have demonstrated experimentally using UCI datasets that our approach can achieve better result than state-of-the-art methods for imbalanced data.

Traditional classification algorithms can be limited in their performance on imbalanced datasets. In recent years, the imbalanced data learning problem has drawn significant interest. In this work, we focus on designing modifications to neural network, in order to appropriately tackle the problem of multiclass imbalance. We propose a method that combines two ideas: diverse random subspace ensemble learning with evolutionary search, to improve the performance of neural network on multiclass imbalanced data. An evolutionary search technique is utilized to optimize the misclassification cost under the guidance of imbalanced data measures. Moreover, the diverse random subspace ensemble employs the minimum overlapping mechanism to provide diversity so as to improve the performance of the learning and optimization of neural network. Furthermore, the ensemble framework can determine the optimal amount of non-redundant components automatically. We have demonstrated experimentally using UCI datasets that our approach can achieve significantly better result than state-of-the-art methods for imbalanced data.

Class imbalance is one of the challenging problems for machine learning in many real-world applications. Many methods have been proposed to address and attempt to solve the problem, including re-sampling and cost-sensitive learning. However, the existing methods have room for improvement since the potentially optimal values of the factors associated with best performance are unknown. Moreover most methods only focus on the binary class imbalance problem, thus there is no efficient solution in multi-class imbalanced learning. This paper presents an effective wrapper framework incorporating the evaluation measure into the objective function of cost sensitive learning as well as re-sampling directly, so as to improve the original methods through optimizing factors influencing the performance on the imbalanced data classification. Comprehensive experimental results on various standard benchmark datasets with different ratios of imbalance show that the influence of optimizing parameters on the solutions for learning imbalanced data is critical, and demonstrate the effectiveness of measure-optimized scheme on the imbalanced data learning.

Association classification has been an important type of the rule-based classification. A variety of approaches have been proposed to build a classifier based on classification rules. In the prediction stage of the extant approaches, most of the existing association classifiers use the ensemble quality measurement of each rule in a subset of rules to predict the class label of the new data. This method still suffers the following two problems. The classification rules are used individually thus the coupling relations between rules [1] are ignored in the prediction. However, in real-world rule set, rules are often inter-related and a new data object may partially satisfy many rules. Furthermore, the classification rule based prediction model lacks a general expression of the decision methodology. This paper proposes a classification method that integrating classification rules into neural network (CRNN, for short), which presents a general form of the rule based decision methodology by rule-based network. In comparison with the extant rule-based classifiers, such as C4.5, CBA, CMAR and CPAR, our approach has two advantages. First, CRNN takes the coupling relations between rules from the training data into account in the prediction step. Second, CRNN automatically obtains higher performance on the structure and parameter learning than traditional neural network. CRNN uses the linear computing algorithm in neural network instead of the costly iterative learning algorithm. Two ways of the classification rule set generation are conducted in this paper for the CRNN evaluation, and CRNN achieves the satisfactory performance.

A common application scenario for WSNs is that the sensor nodes sense the physical environment, process the sensed data and transmit them to remote user periodically. However, in most scenarios, client user is not interested in all the sensed data pushed by the gateway. Too much unnecessary sensed data may make a burden on the communication. In this paper, based on the OSGi (Open Service Gateway Initiative) platform, we propose an extensible and configurable WSN gateway with Data Process Engine. The sensed data can be examined, analyzed and filtered within the Data Process Engine, according to the queries that deployed by the client user. Furthermore, a gateway access method via web browser using XMLSocket is introduced in this paper. Finally we implement and deploy the gateway on the TI OMAP 3530 platform and an implementation scenario has been conducted to confirm the feasibility of this solution in an indoor environment.

Geometric constraint problem can be transformed to an optimization problem. We can solve the problem with niche improved particle swarm. Classical particle swarm optimization is likely to be trapped into local minima as well as premature. A niche improved particle swarm optimization (NIPSO) based on niche theory was developed. After the update of the particle velocity and position, the outlier particle was identified in the NIPSO by comparing the niche number of every particle, with which the crossover and selection operators were employed sequent for those particles, whose personal best values were less than that of the outlier particle. The experiment shows that it can improve the geometric constraint solving efficiency and possess better convergence property than the compared algorithms.

In this paper, we propose a novel medical registration approach based on minimal spanning tree. The proposed approach has the following contributions. (1) Compared with single type of feature points, we extracted corner-like and edge-like points from image, and added a few random points to cover the low contrast regions. (2) Instead of fixing the multi-feature points in the whole procedure, they are hierarchically updated at different registration stages. (3) Based on the feature points, in addition to using pixel intensity, we also added region based feature to include more spatial information. The proposed method is evaluated by performing registration experiments on Brain Web. The experimental results show that the proposed method achieves better robustness while maintaining good registration accuracy, compared to the conventional normalized mutual information (NMI) based registration method.

This paper presented a novel application of Magneto encephalography (MEG) and diffusion tensor image (DTI) on word recognition, in which the spatiotemporal signature and the neural network of brain activation associated with word recognition were investigated. The word stimuli consisted of matched and mismatched words, which were visually and acoustically presented simultaneously. Twenty participants were recruited to distinguish and gave different reactions to these two types of stimuli. The neural activations caused by their reactions were recorded by MEG system and 3T magnetic DTI scanner. Virtual sensor technique and wavelet beam former source analysis, which were state-of-the-art methods, were used to study the MEG and DTI data. Three responses were evoked in the MEG waveform and M160 was identified in the left temporal-occipital junction. All the results coincided with the previous studies' conclusions, which indicated that the integration of virtual sensor and wavelet beam former were effective techniques in analyzing the MEG and DTI data.

In this paper, authors propose a new learning based method for medical image retrieval which is based on fusing different features by linearly combining different similarities. Considering the abundant classes of medical images, this paper avoid to train a classifier for each class by using large amount training data. Instead, by using optimization method to combine different features’ similarity, new method can get good performance while has no much training computation. Experimental results show that the algorithm has potential practical values for clinical routine application.