This repository provides the Pytorch implementations for "Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning"

The WSIs can be found from the TCGA project:

https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga

To extract the patches from the downloaded WSIs, users need to first modify the parameters in get_patches.py (including the WSI paths) and extract the patches by running the following command:

python get_patches.py

After the patch extraction is finished, users can obtain homogeneous and heterogeneous graphs by first edit the configurations in ./configs/GraphConstruction, and specify the correct yaml configuration file in get_graph.py, then run the following command

python get_graph.py

The configurations yaml files for each benchmarking dataset is grouped in respective subfolders. Users may first modify the respective config files for hyper-parameter settings, and update the path to training config in main.py.

python main.py

The training pipeline is mainly written in ./trainer/train_gnn.py. Evaluation is performed after every epoch on validation sets and testing sets. The codes can be find in ./evaluator/eval_homo_graph.py.

The trained checkpoints will be saved in ./chekpoints, including the GNN model. Users can perform evaluation using the saved weights inside the checkpoint.