CDMA
official code for: Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions. MICCAI 2023, early accept arxiv.
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
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
@article{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},
journal={arXiv preprint arXiv:2305.18830},
year={2023}
}
Acknowledgement
The code of semi-supervised learning framework is borrowed from SSL4MIS