From Pixels to Histopathology: A Graph-Based Framework for Interpretable Whole Slide Image Analysis
Journal:
arXiv
Published Date:
Mar 14, 2025
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
