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survival-analysis
multiple-instance-learning
optimal-transport
medical-imaging
deep-learning
pytorch
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Update README.md
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README.md
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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>π MICCAI 2025 π</h4>
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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
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<a href="https://yfwang.me/">Yifan Wang</a><sup>1</sup>
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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/π‘%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 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](
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<br>
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### Evaluation
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You can download
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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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<br>
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## π 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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@
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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>π MICCAI 2025 π</h4>
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<br>
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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>
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<a href="https://yfwang.me/">Yifan Wang</a><sup>1</sup>
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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/π‘%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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## π 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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