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

Alexander Weers, Alexander H Berger, Laurin Lux, Peter J. Schüffler, Daniel Rueckert, Johannes C. Paetzold

Keywords: Whole Slide Image, Computational Pathology, Interpretable AI, Graph Representation Learning

TL;DR: Introduction of a graph-based framework for WSI classification that preserves tissue structures, enhances interpretability, and achieves strong diagnostic performance by replacing regular patches with adaptive, explainable graph representations.

Abstract:

he histopathological analysis of whole-slide images (WSIs) is fundamental to cancer diagnosis but is a time-consuming and expert-driven process.
While deep learning methods show promising results, dominant patch-based methods artificially fragment tissue, ignore biological boundaries, and produce black-box predictions.
We overcome these limitations with a novel framework that transforms gigapixel WSIs into biologically-informed graph representations and is interpretable by design.

Our approach builds graph nodes from tissue regions that respect natural structures, not arbitrary grids.
We introduce an adaptive graph coarsening technique, guided by learned embeddings, to efficiently merge homogeneous regions while preserving diagnostically critical details in heterogeneous areas.
Each node is enriched with a compact, interpretable feature set capturing clinically-motivated priors.
A graph attention network then performs diagnosis on this compact representation.

We demonstrate strong performance on challenging cancer staging and survival prediction tasks.
Crucially, our resource-efficient model ($>$13x fewer parameters and $>$300x less data) achieves results competitive with a massive foundation model, while offering full interpretability through feature attribution.
Our code is publicly available at https://anonymous.4open.science/r/pix2pathology-5680.

Primary Subject Area: Application: Histopathology

Secondary Subject Area: Interpretability and Explainable AI

Registration Requirement: Yes

Reproducibility: https://anonymous.4open.science/r/pix2pathology-5680

Visa & Travel: Yes

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 94