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@@ -28,7 +28,9 @@ model-index:
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  <h2>OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport</h2>
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  <h4>🌟 MICCAI 2025 🌟</h4>
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-
 
 
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  <p>
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  <a href="https://scholar.google.com.hk/citations?user=Tcg-9DcAAAAJ">Qin Ren</a><sup>1 β˜…</sup>&nbsp;
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  <a href="https://yfwang.me/">Yifan Wang</a><sup>1</sup>&nbsp;
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  <p align="center">
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  <a href="https://arxiv.org/abs/2506.20741">
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  <img src="https://img.shields.io/badge/πŸ’‘%20Paper-MICCAI-blue?style=flat-square" alt="Paper">
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- </a>&nbsp;
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  <a href="https://huggingface.co/Y-Research-Group/OTSurv">
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  <img src="https://img.shields.io/badge/Hugging%20Face-Model-yellow?style=flat-square&logo=huggingface" alt="Hugging Face Model">
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- </a>&nbsp;
 
 
 
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  <a href="#">
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  <img src="https://img.shields.io/badge/PyTorch-2.0-EE4C2C?style=flat-square&logo=pytorch" alt="PyTorch 2.0">
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  </a>
@@ -113,7 +118,7 @@ OTSurv/
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  For patch feature extraction, please refer to [CLAM](https://github.com/mahmoodlab/CLAM).
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- You can download the preprocessed features from [this link](#) (link to be provided).
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  <br>
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@@ -152,7 +157,7 @@ bash scripts/train_blca.sh
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  ### Evaluation
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- You can download pre-trained checkpoints from [this link](#) (link to be provided).
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  ```bash
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  # Test results will be saved under result/exp_otsurv_test
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  python analysis/plot_survival_curv.py
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  ```
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- The survival curve for TCGA-BLCA looks like this:
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- <div align="center">
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- <img src="result/visulization/BLCA_km.png" alt="TCGA-BLCA Survival Curve" width="500"/>
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- </div>
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-
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  <br>
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  ## πŸ“Š Performance Results
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  If you find this work useful, please cite our paper:
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  ```bibtex
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- @inproceedings{ren2025otsurv,
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- title={A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport},
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- author={Ren, Qin and Wang, Yifan and Fang, Ruogu and Ling, Haibin and You, Chenyu},
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- booktitle={Medical Image Computing and Computer Assisted Intervention (MICCAI)},
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- year={2025},
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- note={Accepted for publication}
 
 
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  }
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  ```
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  <h2>OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport</h2>
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  <h4>🌟 MICCAI 2025 🌟</h4>
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+
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+ <br>
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+
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  <p>
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  <a href="https://scholar.google.com.hk/citations?user=Tcg-9DcAAAAJ">Qin Ren</a><sup>1 β˜…</sup>&nbsp;
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  <a href="https://yfwang.me/">Yifan Wang</a><sup>1</sup>&nbsp;
 
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  <p align="center">
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  <a href="https://arxiv.org/abs/2506.20741">
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  <img src="https://img.shields.io/badge/πŸ’‘%20Paper-MICCAI-blue?style=flat-square" alt="Paper">
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+ </a>
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  <a href="https://huggingface.co/Y-Research-Group/OTSurv">
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  <img src="https://img.shields.io/badge/Hugging%20Face-Model-yellow?style=flat-square&logo=huggingface" alt="Hugging Face Model">
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+ </a>
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+ <a href="https://huggingface.co/datasets/Y-Research-Group/OTSurv_Dataset">
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+ <img src="https://img.shields.io/badge/Hugging%20Face-Dataset-green?style=flat-square&logo=huggingface" alt="Hugging Face Dataset">
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+ </a>
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  <a href="#">
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  <img src="https://img.shields.io/badge/PyTorch-2.0-EE4C2C?style=flat-square&logo=pytorch" alt="PyTorch 2.0">
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  </a>
 
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  For patch feature extraction, please refer to [CLAM](https://github.com/mahmoodlab/CLAM).
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+ You can download the preprocessed features from [this link](https://huggingface.co/datasets/Y-Research-Group/OTSurv_Dataset).
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  <br>
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  ### Evaluation
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+ You can download all trained checkpoints from [this link](https://huggingface.co/Y-Research-Group/OTSurv).
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  ```bash
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  # Test results will be saved under result/exp_otsurv_test
 
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  python analysis/plot_survival_curv.py
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  ```
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  <br>
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  ## πŸ“Š Performance Results
 
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  If you find this work useful, please cite our paper:
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  ```bibtex
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+ @misc{ren2025otsurvnovelmultipleinstance,
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+ title={OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport},
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+ author={Qin Ren and Yifan Wang and Ruogu Fang and Haibin Ling and Chenyu You},
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+ year={2025},
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+ eprint={2506.20741},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV},
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+ url={https://arxiv.org/abs/2506.20741},
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  }
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  ```
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