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The code is developed using Python 3.7 with PyTorch 1.11.0.
All experiments in our paper were conducted on a single NVIDIA Quadro RTX 6000 with 24G GPU memory.
Install from the requirements.txt using:
pip install -r requirements.txt
1. Data Preparation
1.1. Download data
The original data can be downloaded in following links:
The ISIC dataset includes 2594 dermoscopy images and corresponding annotations.
Split the dataset, resulting in 1815 images for training and 779 images for testing.
python data/split_dataset.py
Then, the dataset is arranged in the following format:
If you find this project useful, please consider citing:
@inproceedings{ijcai2023p467,
title = {Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation},
author = {Shen, Zhiqiang and Cao, Peng and Yang, Hua and Liu, Xiaoli and Yang, Jinzhu and Zaiane, Osmar R.},
booktitle = {Proceedings of the Thirty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-23}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Edith Elkind},
pages = {4199--4207},
year = {2023},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2023/467},
url = {https://doi.org/10.24963/ijcai.2023/467},
}
Contact
If you have any questions or suggestions, please feel free to contact me (xxszqyy@gmail.com).