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+# SAML & A Multi-site Dataset for Prostate MRI Segmentation
+by [Quande Liu](https://github.com/liuquande), [Qi Dou](http://www.cse.cuhk.edu.hk/~qdou/), [Pheng-Ann Heng](http://www.cse.cuhk.edu.hk/~pheng/). 
+
+### Introduction
+
+* The Tensorflow implementation for our MICCAI 2020 paper '[Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains](https://arxiv.org/pdf/2007.02035.pdf)'. 
+
+<p align="center">
+  <img src="figure/saml.png"  width="650"/>
+</p>
+
+* A well-organized multi-site dataset (from six data sources) for prostate MRI segmentation, that can support research in various problem settings with need of multi-site data, such as Domain Generalization, Multi-site Learning and Life-long Learning, etc. For more details and downloading link of the dataset, please [Find Here](https://liuquande.github.io/SAML/).
+    
+
+<p align="center">
+  <img src="figure/protocol.png"  width="650"/>
+</p>
+  
+
+### Setup & Usage for the Code
+
+1. Check dependencies:
+   ```shell
+   python==2.7.17
+   numpy==1.16.6
+   scipy==1.2.1
+   tensorflow-gpu==1.12.0
+   tensorboard==1.12.2
+   SimpleITK==1.2.0
+   ```
+2. To train the model, you need to specify the training configurations (can simply use the default setting) in main.py, then run:
+   ```shell
+   python main.py --phase=train
+   ```
+
+2. To evaluate the model, run:
+   ```shell
+   python main.py --phase=test --restore_model='/path/to/test_model.cpkt'
+   ```
+   You will see the output results in the folder `./output/`.
+
+### Citation
+If this repository is useful for your research, please cite:
+
+```
+@article{liu2020shape,
+  title={Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains},
+  author={Liu, Quande and Dou, Qi and Heng, Pheng-Ann},
+  journal={International Conference on Medical Image Computing and Computer Assisted Intervention},
+  year={2020}
+}
+```
+
+### Questions
+
+For further question about the code or dataset, please contact 'qdliu@cse.cuhk.edu.hk'