Contrastive learning has strong generalization ability and the capability to learn automatically without labeled information. However, it still faces challenges such as insufficient feature diversity, a lack of multi-level semantics, and the balance between tolerance and consistency. To address these challenges, This study propose a Multi-view Hierarchical Graph Contrastive Learning method. First, a new view is generated through a diffusion matrix to provide multi-view data for contrastive learning. Then, these multi-view data are fed into an asynchronous asymmetric network structure, specifically using graph network models to learn diversified features. Next, we adopt a self-designed hierarchical contrastive learning framework, constructing a three-level contrastive loss for joint optimization of nodes, subgraphs, and global graphs. Meanwhile, we introduce alignment and consistency and appropriately adjust the loss function through a temperature coefficient. Ultimately, the model achieves excellent classification performance on multiple datasets through node classification and graph classification tasks.

Due to variations in imaging conditions, images often exhibit discrepancies in color reproduction. Furthermore, motion-induced blur can lead to edge degradation, making color sensitivity and edge blurriness two prevalent and challenging issues in both natural image processing and medical image analysis. To address these challenges, we propose a model termed the Three-View Consistency Mo-mentum Contrastive with Sobel Operator (SVCMC). Specifically, we first design a three-view momen-tum-update architecture that employs a So-bel-augmented ResNet as the backbone. We then introduce a novel contrastive loss, referred to as the Three-View Consistency Momentum Contrastive Loss. Next, to mitigate the oscillations and slow convergence commonly observed in contrastive learning, we construct a dynamic contrastive loss function that adapts in real time over the training process. Finally, we validated the superiority of our model on two medical image datasets and one natural image dataset, where its classification ac-curacy and convergence speed significantly out-performed existing state-of-the-art contrastive models.

Since the regulatory roles of microRNAs in complex human diseases have been gradually demonstrated, more and more enlightening models were developed for predicting microRNA-disease associations. These models can inform bio-logical studies on the differential expression of microRNAs. In this paper, a graph convolutional network-based model using multi-view feature fusion and matrix completion, FMCGCN for brevity, is proposed. First of all, graph autoencoders are used to learn multiple embeddings from different networks. For convenience, attention mechanisms are used to fuse them. As a result, multi-view features constructed from different perspectives are aggregated. Then, the fused embedding is fed into the graph convolutional network to aggregate local information. This fused embedding is thought to facilitate feature extraction for graph convolutional networks. Finally, feature and nuclear norm minimization, a method for matrix completion, is used to obtain the prediction matrix. Additionally, evaluation results under 5-fold cross-validation prove that FMCGCN outperforms current models in several metrics. Furthermore, case studies for esophageal neoplasms and pancreatic neoplasms also demonstrate the validity of our model.

Functional imaging has successfully been applied to capture functional changes in the pathological tissues of a body in recent years. Nuclear medicine functional imaging has been used to acquire information about areas of concerns (e.g. lesions and organs) in a non‐invasive manner, enabling semi‐automated or automated decision‐making for disease diagnosis, treatment, evaluation, and prediction. Focusing on functional nuclear medicine images, in this study, the authors review existing work on the classification of single‐photon emission computed tomography, positron emission tomography, and their hybrid modalities with computed tomography and magnetic resonance imaging images by using convolutional neural network (CNN) techniques. Specifically, they first present an overview of nuclear imaging and the CNN technique, such as nuclear imaging modalities, nuclear image data format, CNN architecture, and the main CNN classification models. According to the diseases of concern, they then classify the existing CNN‐based work on the classification of functional nuclear images into three different categories. For the typical work in each of these categories, they present details about their research objectives, adopted CNN models, and achieved main results. Finally, they discuss research challenges and directions for developing technological solutions to classify nuclear medicine images based on the CNN technique.