🏥 Prostate Cancer Grading using GLAT & IRM

This repository contains the implementation of a Graph Laplacian Attention-Based Transformer (GLAT) integrated with an Iterative Refinement Module (IRM) for prostate cancer grading from whole-slide images (WSIs).

🚀 Key Features

• IRM (Iterative Refinement Module) for adaptive patch selection, ensuring only the most informative tissue regions are selected.
• GLAT (Graph Laplacian Attention Transformer) enforces spatial coherence, preserving histological relationships.
• Convex Aggregation generates a global WSI-level representation, optimizing feature importance.
• State-of-the-art performance on five public and one private dataset.
• Computationally efficient while maintaining high accuracy.

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📂 Project Structure

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📊 Datasets

We evaluated the model on five public and one private (UConn Health) dataset:

Dataset	WSIs Count	Gleason Labels	Notes
TCGA-PRAD	895 WSIs	Gleason Grading	Public dataset from TCGA
SICAPv2	182 WSIs	Gleason Scores	High-quality annotations
GLEASON19	331 TMAs	Pixel-level Annotations	Tissue Microarrays (TMAs)
PANDA	12,625 WSIs	Primary & Secondary Gleason Grades	Largest dataset used
DiagSet	430 WSIs	Prostate Cancer Grading	High-quality dataset
Private Dataset	79 WSIs	Clinical-grade Annotations	Internal dataset

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🛠 Preprocessing

Patches are extracted using ([CLAM] (https://github.com/mahmoodlab/CLAM)) preprocessing pipeline:

• Stain normalization: Reduces staining variability across WSIs.
• Tissue segmentation: Removes irrelevant background regions.
• Patch extraction: Extracts 224×224 patches from WSIs.
• Filtering: Excludes patches with minimal tissue content.

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🏗 Installation

To set up the environment, run:

cd ProstateCancerGrading
pip install -r requirements.txt

## 🛠 **Train the Model**
Run the following command to start training and evaluation:
```bash
python main.py