From Pixels to Histopathology: A Graph-Based Framework for Interpretable Whole Slide Image Analysis

2025-03-14

AI Summary

Conventional patch-based segmentation paradigms for whole-slide image (WSI) classification suffer from loss of global context, disregard of biologically meaningful tissue architecture, and poor interpretability. Method: We propose an end-to-end graph learning framework grounded in histopathological tissue boundaries. Our approach introduces a tissue-aware WSI graph construction scheme and an adaptive graph coarsening mechanism, where nodes correspond to pathologically interpretable tissue regions. Local discriminative features and global topological relationships are jointly encoded via a Graph Attention Network (GAT) to model long-range dependencies. Pathology-level interpretability is achieved through Integrated Gradients. Contribution/Results: The method achieves state-of-the-art performance on cancer staging and survival prediction tasks, significantly enhancing clinical trustworthiness. The source code is publicly available.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Segmentation
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The histopathological classification of whole-slide images (WSIs) is a fundamental task in digital pathology; yet it requires extensive time and expertise from specialists. While deep learning methods show promising results, they typically process WSIs by dividing them into artificial patches, which inherently prevents a network from learning from the entire image context, disregards natural tissue structures and compromises interpretability. Our method overcomes this limitation through a novel graph-based framework that constructs WSI graph representations. The WSI-graph efficiently captures essential histopathological information in a compact form. We build tissue representations (nodes) that follow biological boundaries rather than arbitrary patches all while providing interpretable features for explainability. Through adaptive graph coarsening guided by learned embeddings, we progressively merge regions while maintaining discriminative local features and enabling efficient global information exchange. In our method's final step, we solve the diagnostic task through a graph attention network. We empirically demonstrate strong performance on multiple challenging tasks such as cancer stage classification and survival prediction, while also identifying predictive factors using Integrated Gradients. Our implementation is publicly available at https://github.com/HistoGraph31/pix2pathology

Problem

• Develops graph-based framework for whole-slide image analysis
• Improves interpretability and context learning in histopathology
• Enables efficient cancer stage classification and survival prediction

Innovation

• Graph-based framework for WSI analysis
• Adaptive graph coarsening with learned embeddings
• Graph attention network for diagnostic tasks