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README.md

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-license: cc-by-nc-sa-4.0
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+---
+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>&nbsp;
+    <a href="https://yfwang.me/">Yifan Wang</a><sup>1</sup>&nbsp;
+    <a href="https://lab-smile.github.io/">Ruogu Fang</a><sup>2</sup>&nbsp;
+    <a href="https://scholar.google.com/citations?hl=en&user=v3w4IYUAAAAJ">Haibin Ling</a><sup>1</sup>&nbsp;
+    <a href="https://chenyuyou.me/">Chenyu You</a><sup>1 ★</sup>
+  </p>
+
+  <p>
+    <sup>1</sup> Stony Brook University &nbsp;&nbsp; 
+    <sup>2</sup> University of Florida &nbsp;&nbsp; <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>&nbsp;
+  <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>&nbsp;
+  <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>