Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

TL;DR

This paper reviews how graph neural networks are increasingly used in healthcare data analysis, addressing challenges posed by irregular biological data structures and highlighting future research directions.

Contribution

It provides a systematic overview of graph-based deep learning architectures and their applications in medical diagnosis and analysis, emphasizing recent advances and limitations.

Findings

Graph neural networks effectively model irregular healthcare data.

Applications span functional connectivity, anatomical, and electrical analysis.

Identifies current limitations and future research directions.

Abstract

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 specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.