Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

Deep learning empowers the mainstream medical image segmentation methods.
Nevertheless, current deep segmentation approaches are not capable of efficiently and …

Gross tumor volume (GTV) and clinical target volume (CTV) delineation are two critical steps
in the cancer radiotherapy planning. GTV defines the primary treatment area of the gross …

Cancer research encompasses data across various scales, modalities, and resolutions, from
screening and diagnostic imaging to digitized histopathology slides to various types of …

Automated segmentation of brain glioma plays an active role in diagnosis decision,
progression monitoring and surgery planning. Based on deep neural networks, previous …

The accurate diagnosis of fundus disease can effectively reduce the disease's further
deterioration and provide targeted treatment plans for patients. Fundus image classification …

In pathology, tissue samples are assessed using multiple staining techniques to enhance
contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN …