update readme

readme.md

@@ -1,20 +1,82 @@
 <!-- # Slide-SAM -->
+
 # Slide-SAM: Medical SAM meets sliding window
 
-We upload the SlideSAM-H checkpoint recently!
-Please download by 
 
-Slide-SAM-B: https://pan.baidu.com/s/1jvJ2W4MK24JdpZLwPqMIfA [code：7be9]
 
-SlideSAM-H: https://pan.baidu.com/s/1jnOwyWd-M1fBIauNi3IA4w [code: 05dy] 
-## Before Training
-### install tutils
+<p align="center" width="100%">
+<img src="imgs/training.png"  width="100%" height="60%">
+</p>
+
+
+
+<div align="center">
+    <span class="author-block">
+    <a href="https://scholar.google.com/citations?user=mlTXS0YAAAAJ&hl=en" target="_blank">Quan Quan</a><sup>1,2*</sup>,
+    </span>
+    <span class="author-block">
+    <a href="https://scholar.google.com/citations?user=x1pODsMAAAAJ&hl=en" target="_blank">Fenghe Tang</a><sup>3*</sup>,</span>
+    <span class="author-block">
+    </span>
+    <a href="https://scholar.google.com/citations?user=TxjqAY0AAAAJ&hl=en" target="_blank">Zikang Xu</a><sup>3</sup>,
+    </span>
+    <span class="author-block">
+    <a href="https://scholar.google.com/citations?user=YkfSFekAAAAJ&hl=en" target="_blank">Heqin Zhu</a><sup>3</sup>,
+    </span>
+    <span class="author-block">
+    <a href="https://scholar.google.com/citations?user=8eNm2GMAAAAJ&hl=en" target="_blank">S.Kevin Zhou</a><sup>1,2,3</sup>
+    </span>
+
+<div align="center">
+    <sup>1</sup> <a href='http://english.ict.cas.cn/' target='_blank'>Institute of Computing Technology, Chinese Academy of Sciences</a>
+    <br>
+    <sup>2</sup>
+    <a href='https://english.ucas.ac.cn/' target='_blank'>University of Chinese Academy of Sciences</a>&emsp;
+    </br>
+    <sup>3</sup>
+    <a href='https://en.ustc.edu.cn/' target='_blank'>School of Biomedical Engineering, University of Science and Technology of China</a>&emsp;
+    </br>
+    <sup>*</sup> Equal Contribution&emsp;
+</div>
+
+[![arXiv](https://img.shields.io/badge/arxiv-2311.10121-b31b1b)](https://arxiv.org/pdf/2311.10121.pdf)
+[![github](https://img.shields.io/badge/github-Slide_SAM-black)](https://github.com/Curli-quan/Slide-SAM)
+<a href="#LICENSE--citation">
+  <img alt="License: Apache2.0" src="https://img.shields.io/badge/LICENSE-Apache%202.0-blue.svg"/>
+</a>
+
+## TODOs
+
+- [x] Paper released
+- [x] Code released
+- [x] Slide-SAM-B weights
+- [x] Slide-SAM-H weights
+
+
+
+## Models
+
+### Large scale Medical Image Pretrained Weights
+
+|    name     | resolution |   Prompt    |                           Weights                            |
+| :---------: | :--------: | :---------: | :----------------------------------------------------------: |
+| Slide-SAM-B | 1024x1024  | box & point | [ckpt](https://pan.baidu.com/s/1jvJ2W4MK24JdpZLwPqMIfA)   [code：7be9] |
+| Slide-SAM-H |  224x224   |    78.6     | [ckpt](https://pan.baidu.com/s/1jnOwyWd-M1fBIauNi3IA4w)   [code:  05dy] |
+
+
+
+## Getting Started
+
+### Install tutils tools
+
 ```
 pip install trans-utils
 ```
 
-### prepare datasets
+### Prepare datasets
+
 We recommend you to convert the dataset into the nnUNet format.
+
 ```
 00_custom_dataset
   imagesTr
@@ -24,39 +86,74 @@ We recommend you to convert the dataset into the nnUNet format.
     xxx.nii.gz
     ...
 ```
-try to use the function ```organize_in_nnunet_style``` or ```organize_by_names``` to prepare your custom datasets.
 
-Then run
-```
+Try to use the function organize in  [nnunet-style]([nnUNet/documentation/dataset_format.md at master · MIC-DKFZ/nnUNet (github.com)](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/dataset_format.md)) or ```organize_by_names``` to prepare your custom datasets.
+
+Then run :
+
+```python
 python -m  datasets.generate_txt
 ```
 
 A ```[example]_train.txt``` will be generated in ```./datasets/dataset_list/```
 
 The content should be like below
+
 ```
 01_BCV-Abdomen/Training/img/img0001.nii.gz	01_BCV-Abdomen/Training/label/label0001.nii.gz
 01_BCV-Abdomen/Training/img/img0002.nii.gz	01_BCV-Abdomen/Training/label/label0002.nii.gz
 01_BCV-Abdomen/Training/img/img0003.nii.gz	01_BCV-Abdomen/Training/label/label0003.nii.gz
 ```
 
-### cache 3d data into slices
+### Cache 3d volume into slices
+
 After generating the ```[example]_train.txt``` file, check the config file ```configs/vit_b.yaml```.
 
 Update the params in ```dataset``` by yours. And the ```dataset_list``` should be the name of the generated txt file ```[example]```.
 
 Then run
+
 ```
 python -m datasets.cache_dataset3d
 ```
 
-## Training
-run training
+
+
+## Start Training
+
+Run training on multi-GPU
+
 ```
 CUDA_VISIBLE_DEVICES=0,1,2,3 python -m core.ddp --tag debug
 ```
 
-## Testing
+
+
+## Sliding Inference and Test
+
 ```
 python -m core.volume_predictor
 ```
+
+
+
+<p align="center" width="100%">
+<img src="imgs/inference.png"  width="100%" height="60%">
+</p>
+
+## Citation
+
+If the code, paper and weights help your research, please cite:
+
+```
+@inproceedings{quan2024slide,
+  title={Slide-SAM: Medical SAM Meets Sliding Window},
+  author={Quan, Quan and Tang, Fenghe and Xu, Zikang and Zhu, Heqin and Zhou, S Kevin},
+  booktitle={Medical Imaging with Deep Learning},
+  year={2024}
+}
+```
+
+## License
+
+This project is released under the Apache 2.0 license. Please see the [LICENSE](LICENSE) file for more information.