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PyMIC_examples
PyMIC is a PyTorch-based toolkit for medical image computing with annotation-efficient deep learning. Here we provide a set of examples to show how it can be used for image classification and segmentation tasks. For annotation efficient learning, we show examples of Semi-Supervised Learning (SSL), Self-Supervised Learning (Self-SL), Weakly Supervised Learning (WSL) and Noisy Label Learning (NLL), respectively. For beginners, you can follow the examples by just editting the configuration files for model training, testing and evaluation. For advanced users, you can easily develop your own modules, such as customized networks and loss functions.
News
2024.01 Examples of Self-Supervised Learning have been added.
2024.01 More 2D segmentation networks including SwinUNet and TransUNet have been added.
2023.12 Semi-Supervised Method MCNet has been added to seg_semi_sup/ACDC.
Install PyMIC
The released version of PyMIC (v0.5.0) is required for these examples, and it can be installed by:
pip install PYMIC==0.5.0
To use the latest development version, you can download the source code here, and install it by:
python setup.py install
Data
The datasets for the examples can be downloaded from Google Drive or Baidu Disk (extraction code: xlwg). Extract the files to PyMIC_data after downloading.
List of Examples
Currently we provide the following examples in this repository:
| Catetory | Example | Remarks |
|---|---|---|
| Classification | AntBee | Finetuning a resnet18 for Ant and Bee classification |
| CHNCXR | Finetuning restnet18 and vgg16 for normal/tuberculosis X-ray image classification | |
| Fully supervised segmentation | JSRT | Using five 2D Networks for lung segmentation from chest X-ray images |
| JSRT2 | Using a customized network and loss function for the JSRT dataset | |
| Fetal_HC | Using a 2D UNet for fetal head segmentation from 2D ultrasound images | |
| Prostate | Using a 3D UNet for prostate segmentation from 3D MRI | |
| Semi-supervised segmentation | seg_semi_sup/ACDC | Semi-supervised methods for heart structure segmentation using 2D CNNs |
| seg_semi_sup/AtriaSeg | Semi-supervised methods for left atrial segmentation using 3D CNNs | |
| Weakly-supervised segmentation | seg_weak_sup/ACDC | Segmentation of heart structure with scrible annotations |
| Noisy label learning | seg_noisy_label/JSRT | Comparing different NLL methods for learning from noisy labels |
| Self-Supervised learning | seg_self_sup/lung | Self-Supervised learning methods for pretraining a segmentation model |
Useful links
- PyMIC on Github: https://github.com/HiLab-git/PyMIC
- Usage of PyMIC: https://pymic.readthedocs.io/en/latest/usage.html
Citation
- G. Wang, X. Luo, R. Gu, S. Yang, Y. Qu, S. Zhai, Q. Zhao, K. Li, S. Zhang. (2023). PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation. Computer Methods and Programs in Biomedicine (CMPB). February 2023, 107398.
BibTeX entry:
@article{Wang2022pymic,
author = {Guotai Wang and Xiangde Luo and Ran Gu and Shuojue Yang and Yijie Qu and Shuwei Zhai and Qianfei Zhao and Kang Li and Shaoting Zhang},
title = {{PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation}},
year = {2023},
url = {https://doi.org/10.1016/j.cmpb.2023.107398},
journal = {Computer Methods and Programs in Biomedicine},
volume = {231},
pages = {107398},
}