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ArXiv:
Tags:
survival-analysis
multiple-instance-learning
optimal-transport
medical-imaging
deep-learning
pytorch
License:
| language: en | |
| license: cc-by-nc-sa-4.0 | |
| tags: | |
| - survival-analysis | |
| - multiple-instance-learning | |
| - optimal-transport | |
| - medical-imaging | |
| - deep-learning | |
| - pytorch | |
| model-index: | |
| - name: OTSurv | |
| results: | |
| - task: | |
| type: survival-analysis | |
| name: Survival Prediction | |
| dataset: | |
| type: TCGA | |
| name: TCGA (BLCA, BRCA, LUAD, STAD, COADREAD, KIRC) | |
| metrics: | |
| - type: c-index | |
| value: 0.646 | |
| <div align="center"> | |
| <img src="assets/otsurv_logo.png" alt="OTSurv Logo" width="300"/> | |
| <h2>OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport</h2> | |
| <h4>🌟 MICCAI 2025 🌟</h4> | |
| <p> | |
| <a href="https://scholar.google.com.hk/citations?user=Tcg-9DcAAAAJ">Qin Ren</a><sup>1 ★</sup> | |
| <a href="https://yfwang.me/">Yifan Wang</a><sup>1</sup> | |
| <a href="https://lab-smile.github.io/">Ruogu Fang</a><sup>2</sup> | |
| <a href="https://scholar.google.com/citations?hl=en&user=v3w4IYUAAAAJ">Haibin Ling</a><sup>1</sup> | |
| <a href="https://chenyuyou.me/">Chenyu You</a><sup>1 ★</sup> | |
| </p> | |
| <p> | |
| <sup>1</sup> Stony Brook University | |
| <sup>2</sup> University of Florida <br> | |
| ★ Corresponding authors | |
| </p> | |
| <p align="center"> | |
| <a href="https://arxiv.org/abs/2506.20741"> | |
| <img src="https://img.shields.io/badge/💡%20Paper-MICCAI-blue?style=flat-square" alt="Paper"> | |
| </a> | |
| <a href="https://huggingface.co/Y-Research-Group/OTSurv"> | |
| <img src="https://img.shields.io/badge/Hugging%20Face-Model-yellow?style=flat-square&logo=huggingface" alt="Hugging Face Model"> | |
| </a> | |
| <a href="#"> | |
| <img src="https://img.shields.io/badge/PyTorch-2.0-EE4C2C?style=flat-square&logo=pytorch" alt="PyTorch 2.0"> | |
| </a> | |
| </p> | |
| </div> | |
| ## 🧠 DL;TR | |
| <p> | |
| Welcome to the official repository of <b>OTSurv</b>, a novel framework that integrates | |
| <b>Multiple Instance Learning (MIL)</b> with <b>Heterogeneity-aware Optimal Transport (OT)</b> | |
| to tackle the challenges of survival prediction in medical imaging and clinical data. | |
| </p> | |
| <blockquote> | |
| 📍 <b>To be presented at MICCAI 2025</b><br> | |
| 🧠 <b>Focus</b>: Survival Analysis · Multiple Instance Learning · Optimal Transport | |
| </blockquote> | |
| <div align="center"> | |
| <img src="docs/OTSurv_main.png" alt="OTSurv Framework Overview" width="800"/> | |
| </div> | |
| ## 📁 Data Organization | |
| ### Project Structure | |
| ``` | |
| OTSurv/ | |
| ├── checkpoints/ | |
| │ ├── model_blca_fold0.pth | |
| │ ├── model_blca_fold1.pth | |
| │ └── ... | |
| │ | |
| ├── data/ | |
| │ ├── tcga_blca/ | |
| │ ├── tcga_brca/ | |
| │ ├── tcga_coadread/ | |
| │ ├── tcga_kirc/ | |
| │ ├── tcga_luad/ | |
| │ └── tcga_stad/ | |
| │ | |
| ├── result/ | |
| │ ├── exp_otsurv_test/ | |
| │ ├── exp_otsurv_train/ | |
| │ └── visualization/ | |
| │ | |
| ├── src/ | |
| │ ├── scripts/ | |
| │ ├── analysis/ | |
| │ └── ... | |
| │ | |
| └── docs/ | |
| │ ├── OTSurv_main.png | |
| │ └── OTSurv_heatmap.png | |
| ``` | |
| ### Feature Format | |
| - **H5 Format**: Features are stored in `.h5` files (directories ending with `feats_h5/`) | |
| For patch feature extraction, please refer to [CLAM](https://github.com/mahmoodlab/CLAM). | |
| You can download the preprocessed features from [this link](#) (link to be provided). | |
| <br> | |
| ## 🚀 Quick Start | |
| ### Prerequisites | |
| - Python 3.8+ | |
| - GPU or CPU-only | |
| - Conda package manager | |
| ### Installation | |
| ```bash | |
| # Clone the repository | |
| git clone https://github.com/Y-Research-SBU/OTSurv.git | |
| cd OTSurv | |
| # Create conda environment | |
| conda env create -f env.yaml | |
| conda activate otsurv | |
| ``` | |
| ### Training | |
| ```bash | |
| # Training results will be saved under result/exp_otsurv_train | |
| cd src | |
| # Train on all datasets | |
| bash scripts/train_otsurv.sh | |
| # Train on TCGA-BLCA dataset specifically | |
| bash scripts/train_blca.sh | |
| ``` | |
| ### Evaluation | |
| You can download pre-trained checkpoints from [this link](#) (link to be provided). | |
| ```bash | |
| # Test results will be saved under result/exp_otsurv_test | |
| cd src | |
| # Test on all datasets | |
| bash scripts/test_otsurv.sh | |
| # Test on TCGA-BLCA dataset specifically | |
| bash scripts/test_blca.sh | |
| ``` | |
| ```bash | |
| cd src | |
| # Calculate performance metrics | |
| python analysis/calculate_CIndex_mean_std.py | |
| ``` | |
| ```bash | |
| # Generated figures will be saved under result/visualization | |
| cd src | |
| # Generate survival curves | |
| python analysis/plot_survival_curv.py | |
| ``` | |
| The survival curve for TCGA-BLCA looks like this: | |
| <div align="center"> | |
| <img src="result/visulization/BLCA_km.png" alt="TCGA-BLCA Survival Curve" width="500"/> | |
| </div> | |
| <br> | |
| ## 📊 Performance Results | |
| Below are the C-Index performance results of OTSurv across different cancer types: | |
| | Cancer Type | Mean C-Index | Std Dev | | |
| |-------------|-------------|---------| | |
| | **BRCA** | 0.621 | ±0.071 | | |
| | **BLCA** | 0.637 | ±0.065 | | |
| | **LUAD** | 0.638 | ±0.077 | | |
| | **STAD** | 0.565 | ±0.057 | | |
| | **COADREAD** | 0.667 | ±0.111 | | |
| | **KIRC** | 0.750 | ±0.149 | | |
| **Overall Performance**: Average C-Index across all datasets is **0.646** | |
| > 💡 **Note**: C-Index (Concordance Index) is a commonly used performance metric in survival analysis, where values closer to 1.0 indicate better prediction performance. | |
| <br> | |
| ## 📚 Citation | |
| If you find this work useful, please cite our paper: | |
| ```bibtex | |
| @inproceedings{ren2025otsurv, | |
| title={A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport}, | |
| author={Ren, Qin and Wang, Yifan and Fang, Ruogu and Ling, Haibin and You, Chenyu}, | |
| booktitle={Medical Image Computing and Computer Assisted Intervention (MICCAI)}, | |
| year={2025}, | |
| note={Accepted for publication} | |
| } | |
| ``` | |
| > 📝 **Note**: This paper has been accepted at MICCAI 2025. The citation details will be updated once the paper is officially published. | |
| > | |
| <br> | |
| ## 🙏 Acknowledgements | |
| This work builds upon the excellent research from: | |
| - [PANTHER](https://openaccess.thecvf.com/content/CVPR2024/html/Song_Morphological_Prototyping_for_Unsupervised_Slide_Representation_Learning_in_Computational_Pathology_CVPR_2024_paper.html) | |
| - [MMP](https://github.com/mahmoodlab/MMP) | |
| - [CLAM](https://github.com/mahmoodlab/CLAM) | |
| - [PPOT](https://github.com/rhfeiyang/PPOT) | |
| <br> | |
| ## 📄 License | |
| This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License - see the [LICENSE.md](LICENSE.md) file for details. | |
| <br> | |
| ## 🤝 Contributing | |
| We welcome contributions to **OTSurv**! If you have suggestions, bug reports, or want to add features or experiments, feel free to: | |
| - 🐞 Submit an issue | |
| - 🔧 Open a pull request | |
| - 💬 Start a discussion | |
| --- | |
| <p align="center"> | |
| ⭐ <strong>If you find this repository helpful, please consider starring it!</strong> ⭐ | |
| </p> |