Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by mental abnormalities and motor dysfunction. Its early classification and prediction of clinical scores have been major concerns for researchers. Currently, multi-view data learning has become an essential research area due to the capacity of multiple views to provide complementary insights from various perspectives. However, the discontinuous distribution, data missing complexity, small sample size, and redundant features in multi-view datasets pose a substantial obstacle, and most existing multi-view learning methods are unable to handle these challenges effectively. In this study, we propose a novel incomplete multi-view data learning framework (IMVDL) via dynamic embedding and partiall2,1norm constraints for PD diagnosis. Specifically, multi-view dynamic embedding can adapt to any view missing scene, thereby linearly/nonlinearly mapping incomplete multi-view data to low-dimensional manifold spaces and generating complete multi-view data representations. The partiall2,1norm constraint can ignore larger feature weight values and performl2,1norm sparse on the remaining weights, thereby avoiding the sparse bias problem caused by larger weight values. An efficient iterative algorithm is derived to find the optimal solution of the IMVDL method. We conduct extensive experiments using multi-modal neuroimage data from the Parkinson's Progression Markers Initiative (PPMI) database. The results demonstrate that the IMVDL method is superior to other comparative methods. The source code for IMVDL is available at https://github.com/a610lab/IMVDL/.

An accurate assessment of the fetal brain's biological age is crucial for identifying and interpreting key neurodevelopmental milestones. Recently, deep learning techniques have been applied to fetal brain Magnetic Resonance Imaging (MRI) data to improve the precision of fetal brain age prediction. However, data privacy concerns and legal constraints have given rise to significant data silos, impeding data sharing, and limiting the accuracy and robustness of models trained on small, localized datasets. To overcome these challenges, we propose a novel Tri-Factor Collaborative Optimization Federated Learning (TFCO-FL) framework, which enables collaborative analysis of fetal MRI from multiple medical centers while ensuring strong data privacy. TFCO-FL incorporates a comprehensive evaluation mechanism that quantifies client contributions based on three factors: gradient information, cycle learning efficiency, and data quality. This approach effectively addresses non-independent and identically distributed (Non-IID) issues caused by heterogeneous and unevenly sized datasets.

Accurate prediction of fetal brain age from MRI is vital for assessing fetal development. However, clinical MRI data are often imbalanced across gestational ages, causing deep learning models to bias toward overrepresented samples and limiting generalization. Existing methods mainly adjust sample weights or smooth distributions but ignore ordinal structure consistency between feature and label spaces. To address this, we propose an adaptive multi-scale permutation entropy regularizer (AMPER) that enforces ordinal consistency between feature and label similarity sequences. AMPER introduces permutation entropy as a regularizer for deep regression, alleviating label imbalance bias and improving prediction reliability. It integrates (1) an adaptive scale selection mechanism guided by label entropy to ensure flexible regularization under varying data distributions, and (2) multi-scale permutation entropy to capture both local and global ordinal structures. Experiments show that AMPER achieves a mean absolute error of 0.793±0.024 weeks and an R2of 0.936±0.005, demonstrating its effectiveness for imbalanced regression and clinical potential for accurate fetal brain age estimation.

Alzheimer's Disease (AD) progression involves complex pathological cascades through brain networks. Current neuroimaging approaches analyze connectivity modalities in isolation, lack frameworks for integrating clinical knowledge, and ignore directional causal relationships. We propose PKFA-GCN, a multimodal graph convolutional network through: (1) Feature Space Embedding Alignment (FSEA) for cross-modal fusion, (2) Prior knowledge-guided region selection, and (3) Effective connectivity integration. Experiments on ADNI dataset (396 subjects) achieve: 94.56% (AD vs NC),$85.67\%$(AD vs MCI), 91.34% (MCI vs NC), and 86.12% (three-way classification), outperforming 12 methods. Visualization analyses confirm consistency with known AD pathophysiology.

The classification of carotid plaques is crucial for assessing the risk of cardiovascular diseases. Recently, deep learning has emerged as a promising solution for automatic carotid plaque classification. However, its data-driven nature makes models prone to bias from noisy labels, often caused by inconsistent and unreliable labeling, which can significantly degrade model performance. To address this issue, this paper proposes a robust carotid plaque classification method (TMSS) designed to effectively learn from datasets with noisy labels using transition matrix and structural similarity regularization. The general framework of TMSS comprises an unsupervised constructive learning branch and a supervised classification branch, regularized by structural similarity to maximize the agreement of sample relationships between the feature space and the prediction space. The transition matrix is learned to further correct the noisy labels in the classifier branch. Evaluated on 1,270 carotid ultrasound images, experimental results demonstrate that TMSS achieves significant improvements in classification performance over state-of-the-art approaches. The proposed method enhances diagnostic accuracy by mitigating label noise and improving model robustness in carotid plaque classification, thereby facilitating early disease detection and enabling personalized treatment.

The segmentation and classification of carotid plaques in ultrasound images play important roles in the treatment of atherosclerosis and assessment for stroke risk. Although deep learning methods have been used for carotid plaque segmentation and classification, two-stage methods will increase the complexity of the overall analysis and the existing multi-task methods ignored the relationship between the segmentation and classification. These will lead to suboptimal performance as valuable information might not be fully leveraged across all tasks. Therefore, we propose a multi-task learning framework (RCCM-Net) for ultrasound carotid plaque segmentation and classification, which utilizes a region confidence module (RCM) and a sample category confidence module (CCM) to exploit the correlation between these two tasks. The RCM provides knowledge from the probability of plaque regions to the classification task, while the CCM is designed to learn the categorical sample weight for the segmentation task. A total of 1270 2D ultrasound images of carotid plaques were collected from Zhongnan Hospital (Wuhan, China) for our experiments. The results showed that the proposed method can improve both segmentation and classification performance compared to existing single-task networks (i.e., SegNet, Deeplabv3+, UNet++, EfficientNet, Res2Net, RepVGG, DPN) and multi-task algorithms (i.e., HRNet, MTANet), with an accuracy of 85.82% for classification and a Dice-similarity-coefficient of 84.92% for segmentation. In the ablation study, the results demonstrated that both the designed RCM and CCM were beneficial in improving the network's performance. Therefore, we believe that the proposed method could be useful for carotid plaque analysis in clinical practice.

Alzheimer’s disease is a neurodegenerative disorder that gradually impairs cognitive abilities. Early detection, diagnosis, and treatment are crucial for slowing the progression of the disease. In the diagnosis of Alzheimer’s disease, Graph Convolutional Networks (GCN) provide a powerful tool to enhance accuracy. However, the training of GCN faces challenges due to the tedious annotation process and limited data.To address this issue, we employ contrastive learning for pre-training GCN to improve classification performance under limited data conditions. Firstly, we augment graph data through singular value decomposition, preserving the brain’s primary topological structure and avoiding the loss of intrinsic semantic structure during augmentation. Secondly, we design a Soft-Clustering GCN to obtain more robust representations of brain data. Lastly, our framework clusters graphs with similar feature semantics into the same group and encourages clustering consistency between different augmentations of the same graph. In negative sampling, we select graphs from different groups as negative samples to ensure semantic differences between positive and negative samples. Experimental results demonstrate that our approach outperforms state-of-the-art methods on the Alzheimer’s disease dataset.

Parkinson’s disease (PD) is a common neurodegenerative disorder in the elderly population. The progressive symptoms of PD can have significant physical and economic implications for patients. Therefore, the development of a method to aid in the diagnosis and prediction of PD is crucial. However, medical neuroimaging data often have redundant features and high data dimensions, which can negatively impact algorithm accuracy. To solve this challenge, a supervised algorithm for feature selection is proposed for the early diagnosis and prediction of PD. Specifically, the proposed method incorporates adaptive learning during iterations, which allows adaptive updating of the similarity matrix and selection of informative features. Meanwhile, we introduce flexible mapping to address the limitation that linear mapping is too strict. To measure the effectiveness of the algorithm, we test it on the Parkinson’s Progression Markers Initiative (PPMI) public dataset. According to the outcomes of the experiment, the proposed method outperforms the competing feature selection methods and graph neural network approaches.

The analysis of plaque region in carotid ultrasound images is crucial for determining and assessing the harm-fulness of carotid plaques. Carotid ultrasound images provide both the location and status information of plaques, which can help diagnose carotid atherosclerosis. Despite this, the relationship between various plaque tasks has been disregarded in prior research, and due to the significant expense associated with manual image segmentation, there is a shortage of datasets that contain a substantial quantity of manually annotated plaque regions. In this paper, a semi-supervised learning algorithm is proposed to reduce reliance on annotated data, and due to the correlation between the plaque classification task and the plaque region semantic segmentation task, an auxiliary learning method named SAL-Net is proposed. The primary task of this model is supervised plaque classification, while the auxiliary task is a semi-supervised semantic segmentation task. The experiments are carried out on a carotid ultrasound image dataset, and the results show that SAL-net can effectively utilize the correlation between different tasks to improve the performance of the model.

Image Registration(IR) has been widely applied in biomedical image processing. It is the process of finding an optimal geometric transformation to align two images, which could be defined as a parameter optimization issue. Genetic Algorithm(GA) is one of the most efficient methods for solving complex optimization problems and it has been applied to the real-coding IR problem. However, the classical GA suffers from premature convergence and is easy to fall into local optimum. Inspired by heterosis, which is a common phenomenon in biology, this study proposes an improved GA. By artificially simulating the breeding process of Chinese three-line hybrid rice, known as a successful application of heterosis, the original crossover and mutation mechanisms of GA are improved, and a dynamic diversity controller is designed. This study conducts several multimodal biomedical IR experiments to compare the improved GA with state-of-the-art IR methods, the results show that the proposed method outperforms the others in most scenarios with faster convergence speed and greater robustness.

A locally adaptive shrinkage Bayesian estimate for medical ultrasonography denoising is proposed by exploiting the correlation among image sparse coding. The Laplacian distribution is used to model the coding coefficients. The paper deduces the MAP estimate formula and adaptive threshold. Simulation experiments are carried out to show the effectiveness of the new method. Results demonstrate that compared with classical denoising algorithms, the new method has increased peak signal-to-noise ratio (PSNR) and improved the quality of subjective visual effect. Our algorithm is also proved to be effective to the medical ultrasonography.

Face recognition has attracted considerable concerns in recent years. In practical applications, there are generally a small amount of labeled face images and a lot of unlabeled ones can be available. In this paper, we introduce a semi-supervised face recognition method where semi-supervised LDA (SDA) and Affinity Propagation (AP) are integrated into Self-training. SDA is employed to update the face subspace using labeled and unlabeled face images. And we employ AP to computer the templates which exist in the original face images. A series of experiments on three face datasets are carried out to evaluate the performance of our algorithm. Experimental results illustrate that our algorithm outperforms the other unsupervised, semi-supervised and supervised methods.

A locally adaptive Bayesian estimate for image denoising is proposed by exploiting the correlation among image shear let coefficients in a sub-band. The Laplacian distribution can model a wide range of process, from heavy-tailed to less heavy-tailed processes. This paper deduces Laplacian prior distribution based the MAP estimate formula and sub-band adaptive threshold. Finally, a simulation is carried out to show the effectiveness of the new estimate. Experiment results demonstrate that compared with classical sub-band adaptive algorithms, the new denoising method has significantly increased peak signal-to-noise ratio (PSNR) and improved the quality of subjective visual effect.