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MICCAI 2023, early accept [arxiv](https://arxiv.org/abs/2305.18830).\nAnd the extension is published on the [Pattern Recognition](https://www.sciencedirect.com/science/article/pii/S0031320324002437) 2024.\n\n### Overall Framework\nThere are three branches based on different attention mechanisms and two losses in our framework\n![overall](https://github.com/HiLab-git/CDMA/blob/main/pics/overall2.png)\n\n### usage\nFirst, split the dataset into train, val and test sets, then crop WSIs into patches for computational feasibility.\n```\npython utils.move_file.py\npython slide_window.py\n```\n\nThen, just use the ```run.sh``` script to run the code.\n```\nsh run.sh\n```\n\n### Data Acquisition\nThe DigestPath dataset can be downloaded in: [DigestPath](https://digestpath2019.grand-challenge.org/)\n\nThe dataset dir is like this after splitting and cropping:\n```\ndigestpath2019\n-----tissue-train-100\n-----tissue-train-100-patch\n-----tissue-train-5\n-----tissue-train-5-patch\n-----tissue-val\n-----tissue-val-patch\n-----tissue-test\n```\n\nYou can get data lists in ```data/digestpath```\n### Citation\n```\n@inproceedings{zhong2023semi,\n  title={Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions},\n  author={Zhong, Lanfeng and Liao, Xin and Zhang, Shaoting and Wang, Guotai},\n  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},\n  pages={570--579},\n  year={2023},\n  organization={Springer}\n}\n\n@article{zhong2024semi,\n  title={Semi-supervised pathological image segmentation via cross distillation of multiple attentions and Seg-CAM consistency},\n  author={Zhong, Lanfeng and Luo, Xiangde and Liao, Xin and Zhang, Shaoting and Wang, Guotai},\n  journal={Pattern Recognition},\n  pages={110492},\n  year={2024},\n  publisher={Elsevier}\n}\n```\n\n### Acknowledgement\nThe code of semi-supervised learning framework is borrowed from [SSL4MIS](https://github.com/HiLab-git/SSL4MIS)\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhilab-git%2Fcdma","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhilab-git%2Fcdma","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhilab-git%2Fcdma/lists"}