https://github.com/hilab-git/cdma
offical code for: Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions. MICCAI 2023.
https://github.com/hilab-git/cdma
Last synced: about 1 year ago
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offical code for: Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions. MICCAI 2023.
- Host: GitHub
- URL: https://github.com/hilab-git/cdma
- Owner: HiLab-git
- Created: 2023-03-09T07:19:38.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-17T15:14:44.000Z (about 2 years ago)
- Last Synced: 2025-03-20T21:39:03.557Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 644 KB
- Stars: 53
- Watchers: 2
- Forks: 4
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
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README
# CDMA
official code for: Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions. MICCAI 2023, early accept [arxiv](https://arxiv.org/abs/2305.18830).
And the extension is published on the [Pattern Recognition](https://www.sciencedirect.com/science/article/pii/S0031320324002437) 2024.
### Overall Framework
There are three branches based on different attention mechanisms and two losses in our framework
### usage
First, split the dataset into train, val and test sets, then crop WSIs into patches for computational feasibility.
```
python utils.move_file.py
python slide_window.py
```
Then, just use the ```run.sh``` script to run the code.
```
sh run.sh
```
### Data Acquisition
The DigestPath dataset can be downloaded in: [DigestPath](https://digestpath2019.grand-challenge.org/)
The dataset dir is like this after splitting and cropping:
```
digestpath2019
-----tissue-train-100
-----tissue-train-100-patch
-----tissue-train-5
-----tissue-train-5-patch
-----tissue-val
-----tissue-val-patch
-----tissue-test
```
You can get data lists in ```data/digestpath```
### Citation
```
@inproceedings{zhong2023semi,
title={Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions},
author={Zhong, Lanfeng and Liao, Xin and Zhang, Shaoting and Wang, Guotai},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={570--579},
year={2023},
organization={Springer}
}
@article{zhong2024semi,
title={Semi-supervised pathological image segmentation via cross distillation of multiple attentions and Seg-CAM consistency},
author={Zhong, Lanfeng and Luo, Xiangde and Liao, Xin and Zhang, Shaoting and Wang, Guotai},
journal={Pattern Recognition},
pages={110492},
year={2024},
publisher={Elsevier}
}
```
### Acknowledgement
The code of semi-supervised learning framework is borrowed from [SSL4MIS](https://github.com/HiLab-git/SSL4MIS)