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ProtoGTX: Prototypical Graph Transformer for Interpretable WSI Analysis

ProtoGTX is a novel Graph Transformer designed for interpretable computational pathology. By synergizing spatial topology and morphological semantics, it delivers high-performance diagnostic predictions with transparent, clinically-aligned explanations.


📂 Repository Structure

  • protogtx/: Core architecture of the Prototypical Graph Transformer.
  • graph_builder/: Modules for constructing spatial graphs from WSI patches.
  • dataset_module/: Graph-based data loaders for efficient WSI processing.
  • visualization/: Utilities for generating dual-modality (GraphCAM & Prototype) interpretability maps.
  • example/: Step-by-step interactive tutorials and evaluation demos.

🚀 Getting Started

Follow our step-by-step tutorials in the example/ directory.

Interactive Tutorials (Jupyter Notebooks)

We provide a pipeline from graph construction to final evaluation:

  1. 1_build_grid_graphs.ipynb
    • Learn how to transform a Whole Slide Image (WSI) into a grid-based spatial graph representation.
  2. 2_define_prototype_features.ipynb
    • Explore how latent features are mapped to an interpretable vocabulary of morphological prototypes.
  3. 3_prediction_evaluation.ipynb
    • Perform model inference on a representative LUAD (Lung Adenocarcinoma) case and visualize the dual-modality explanations.

About

An interpretable graph-based representation model for pathology whole slide images (WSI) leveraging patch-level relations and prototype learning.

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