Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …

In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks and graph transformer architectures, stands out for its capability to capture …

In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks (GNNs), stands out for its capability to capture intricate relationships within …

Motivation Accurately predicting cancer survival is crucial for helping clinicians to plan
appropriate treatments, which largely improves the life quality of cancer patients and spares …

Although identifying high-cost inpatients with ischemic heart disease (IHD) at the point of
admission is helpful for timely intervention and reducing costs, it is a difficult task due to the …

Multimodality Representation Learning, as a technique of learning to embed information
from different modalities and their correlations, has achieved remarkable success on a …

Traditional approaches to predicting breast cancer patients' survival outcomes were based
on clinical subgroups, the PAM50 genes, or the histological tissue's evaluation. With the …

In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks
(GNNs), a domain where deep learning-based approaches have increasingly shown …

Simple Summary Graph Neural Networks are emerging as a powerful tool for structured data
analysis, and predictive modeling in massive multimodal datasets. In this review, we survey …

Recently, lung cancer prediction based on imaging genomics has attracted great attention.
However, such studies often have many challenges, such as small sample size, high …