Graph Neural Networks in Histopathology: Emerging Trends and Future Directions

TL;DR

This paper reviews the application of Graph Neural Networks (GNNs) in histopathology, highlighting emerging trends like hierarchical and multimodal GNNs, and discusses future research directions to enhance tissue analysis.

Contribution

It provides a comprehensive survey of GNN applications in histopathology, identifies key emerging trends, and proposes future directions for advancing the field.

Findings

Identification of four emerging GNN trends: Hierarchical, Adaptive, Multimodal, Higher-order

Quantitative analysis of literature showing rapid growth of GNNs in histopathology

Guidance for future research and methodological development

Abstract

Histopathological analysis of Whole Slide Images (WSIs) has seen a surge in the utilization of deep learning methods, particularly Convolutional Neural Networks (CNNs). However, CNNs often fall short in capturing the intricate spatial dependencies inherent in WSIs. Graph Neural Networks (GNNs) present a promising alternative, adept at directly modeling pairwise interactions and effectively discerning the topological tissue and cellular structures within WSIs. Recognizing the pressing need for deep learning techniques that harness the topological structure of WSIs, the application of GNNs in histopathology has experienced rapid growth. In this comprehensive review, we survey GNNs in histopathology, discuss their applications, and explore emerging trends that pave the way for future advancements in the field. We begin by elucidating the fundamentals of GNNs and their potential applications in histopathology. Leveraging quantitative literature analysis, we identify four emerging trends: Hierarchical GNNs, Adaptive Graph Structure Learning, Multimodal GNNs, and Higher-order GNNs. Through an in-depth exploration of these trends, we offer insights into the evolving landscape of GNNs in histopathological analysis. Based on our findings, we propose future directions to propel the field forward. Our analysis serves to guide researchers and practitioners towards innovative approaches and methodologies, fostering advancements in histopathological analysis through the lens of graph neural networks.