| # 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. |
|
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| ### Overall Framework |
| There are three branches based on different attention mechanisms and two losses in our framework |
|  |
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| ### 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/) |
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| 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) |
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