This repository contains the source for the paper Inter-Intra Hypergraph Computation for Survival Prediction on Whole Slide Images accepted by IEEE TPAMI by Xiangmin Han, Huijian Zhou, Zhiqiang Tian, Shaoyi Du, Yue Gao.

Introduction

In this repository, we provide the training code for Intra-Hypergraph and Inter-Hypergraph models, along with various methods for hypergraph structure modeling. The dataset includes a sample list from publicly available datasets, which can be downloaded directly from TCGA.

(a) Multi-level medical information and corresponding correlations. Each subject contains multi-level medical information, such as intra-correlation at the cell and tumor level, and the inter-correlation at the group level. (b) Existing graph-based and MIL-based WSI analysis methods. (c) Different hypergraph modeling methods for WSI, the boundary-wise topological hypergraph models the boundary of WSI as hyperedges, the Spatial-wise Topological hypergraph considers the location interaction of patches as hyperedges, and the global-feature semantic hypergraph computes the feature distance to model the hyperedges.

The pipeline of inter-intra hypergraph computation framework.

Training Data Structure

• DIR: config
  • xx.yaml (your train/test config file)
• DIR: get_feature
  • sampled_vis (sampled patches, only for visualization)
  • patch_ft (deep features extracted via CNN models)
  • patch_coor (coordinates of the sampled patches, only for visualization)

Training

1. Feature Extraction

This script will generate three types of files: sampled_vis, patch_ft, and patch_coor.

WSI_sample_patch.py

2. Training Intra-HGNN

You can train the Intra-HGNN model to obtain intra-embeddings and intra-risk.

Note that this module can be used independently.

python train_stage1_intra.py  

3. Training Inter-HGNN

You can train the Inter-HGNN model to fuse intra- and inter-risks for the final result.

Note that if you have defined the feature vectors of inter-vertices in the inter-hypergraph, you can train this module without the first stage.

python train_stage2_inter.py

Citation

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

@article{han2025iihgc,
  title={Inter-intra hypergraph computation for survival prediction on whole slide images},
  author={Xiangmin, Han and Huijian, Zhou and Zhiqiang, Tian and Shaoyi, Du and Yue, Gao},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2025},
  publisher={IEEE}
}

Contact

IIHGC is maintained by iMoon-Lab, Tsinghua University. If you have any questions, please feel free to contact us via email: Xiangmin Han.