Combining Graph Neural Network and Mamba to Capture Local and Global Tissue Spatial Relationships in Whole Slide Images

TL;DR

This paper presents a novel model combining graph neural networks and a state space model to effectively capture local and global spatial relationships in whole slide images for improved prediction of patient outcomes.

Contribution

The work introduces a new GNN and Mamba-based model that enhances spatial feature aggregation in WSIs, outperforming existing methods.

Findings

Improved prediction accuracy for lung adenocarcinoma progression-free survival.

Effective integration of local and global spatial relationships.

Model flexibility for extension to other WSI analyses.

Abstract

In computational pathology, extracting spatial features from gigapixel whole slide images (WSIs) is a fundamental task, but due to their large size, WSIs are typically segmented into smaller tiles. A critical aspect of this analysis is aggregating information from these tiles to make predictions at the WSI level. We introduce a model that combines a message-passing graph neural network (GNN) with a state space model (Mamba) to capture both local and global spatial relationships among the tiles in WSIs. The model's effectiveness was demonstrated in predicting progression-free survival among patients with early-stage lung adenocarcinomas (LUAD). We compared the model with other state-of-the-art methods for tile-level information aggregation in WSIs, including tile-level information summary statistics-based aggregation, multiple instance learning (MIL)-based aggregation, GNN-based aggregation, and GNN-transformer-based aggregation. Additional experiments showed the impact of different types of node features and different tile sampling strategies on the model performance. This work can be easily extended to any WSI-based analysis. Code: https://github.com/rina-ding/gat-mamba.