HeLongHuang/SHGNN

Spatial-hierarchical Graph Neural Network with Dynamic Structure Learning for Histological Image Classification

Installation

Clone the repo:

git clone https://github.com/HeLongHuang/SHGNN && cd SHGNN

Create a conda environment and activate it:

conda create -n env python=3.8
conda activate env
pip install -r requirements.txt

Data Preprocess

gendata.ipynb shows how to transform a histological image into the hierarchical graphs. After the data processing is completed, put all hierarchical graphs into a folder. The form is as follows:

PYG_Data
   └── Dataset
          ├── pyg_data_1.pkl
          ├── pyg_data_2.pkl
                    :
          └── pyg_data_n.pkl

You also can download the processed graph data here. The access code is: dei3

Training

First, setting the data splits and hyperparameters in the file train.py. Then, experiments can be run using the following command-line:

cd train
python train_<experiments>.py

The trained model will be saved in the folder SavedModels.

Saved models

We provide a 3-fold checkpoint for each dataset, which performing as:

Dataset	Macro AUC
CRCS	96.15
BRACS	95.06

Inference

Using the following command-line for model inference and result statistics:

cd inference
python inference_<experiments>.py

More Info

Our implementation refers the following publicly available codes.
- Pytorch Geometric--Fey M, Lenssen J E. Fast graph representation learning with PyTorch Geometric[J]. arXiv preprint arXiv:1903.02428, 2019.
- Histocartography--Jaume G, Pati P, Anklin V, et al. HistoCartography: A toolkit for graph analytics in digital pathology[C]//MICCAI Workshop on Computational Pathology. PMLR, 2021: 117-128.
- Hact-net--Pati P, Jaume G, Foncubierta-Rodríguez A, et al. Hierarchical graph representations in digital pathology[J]. Medical image analysis, 2022, 75: 102264.
- ViT Pytorch--Dosovitskiy A, Beyer L, Kolesnikov A, et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale[C]//International Conference on Learning Representations. 2020.
Our in-house dataset (CRCS) cannot be made public due to the data use agreement and the protection of patients' privacy.
The BRACS dataset is available at:
- BRACS--Brancati N, Anniciello A M, Pati P, et al. Bracs: A dataset for breast carcinoma subtyping in h&e histology images[J]. arXiv preprint arXiv:2111.04740, 2021.
Results on BRACS with official data split:
- Under the same experimental setup, we further validated our model on BRACS with official data split. The pretrained checkpoints of three random repeated experiments are provided, which performing as:
  
  Metrics Macro AUC Weighted F1
  
  Average 89.65 62.77

Metrics	Macro AUC	Weighted F1
Average	89.65	62.77

Citation

If you found our work useful in your research, please consider citing our work at:

@InProceedings{10.1007/978-3-031-16434-7_18,
author="Hou, Wentai
and Huang, Helong
and Peng, Qiong
and Yu, Rongshan
and Yu, Lequan
and Wang, Liansheng",
title="Spatial-Hierarchical Graph Neural Network with Dynamic Structure Learning for Histological Image Classification",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="181--191",
}

HeLongHuang / SHGNN