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/.

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.

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.

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.

Carotid plaque classification is of great significance for the diagnosis and treatment of carotid artery disease and the prediction of ischemic stroke. However, the ultrasound image structure of carotid plaques is complex, and manual labeling is time-consuming, which results in lots of unlabeled images. Semi-supervised methods can be well applied in this field by using unlabeled samples to improve model performance. Most of the existing semi-supervised methods are based on pseudo-labels and consistency regularization; however, these kinds of methods will cause error pseudo-labels during sample selection and an imbalanced distribution of pseudo-labels due to cognitive biases in model training. To solve these problems, We propose a method based on feature correction and pseudo-label balance correction, which utilizes a semi-supervised approach. The feature correction module uses the features of labeled data to correct the predicted probabilities of unlabeled data and improve the discrimination of pseudo-labels. The pseudo-label balance correction module can dynamically adjust the sample weight in the loss function according to the number of pseudo-labels to alleviate the problem of data imbalance. Evaluated on 1270 ultrasound carotid plaque images, our method achieved superior performance to other state-of-the-art methods (i.e., Mixmatch, Fixmatch, and Flexmatch) on 10%, 30%, and 50% labeled training data. Our method demonstrated accurate carotid plaque classification using a small number of labeled training datasets, potentially benefiting clinical practice and trials.