Welcome to PyMIC’s documentation!

PyMIC is a pytorch-based toolkit for medical image computing with annotation-efficient deep learning. PyMIC is developed to support learning with imperfect labels, including semi-supervised and weakly supervised learning, and learning with noisy annotations.

Check out the Installation section for install PyMIC, and go to the Usage section for understanding modules for the segmentation pipeline designed in PyMIC. Please follow PyMIC_examples to quickly start with using PyMIC.

Note

This project is under active development. It will be updated later.

Getting Started

• Installation
• Usage
  • Quick Start
  • Fully Supervised Learning
  • Semi-Supervised Learning
  • Weakly-Supervised Learning
  • Noisy Label Learning
• API
  • pymic.io package
  • pymic.layer package
  • pymic.loss package
  • pymic.net package
  • pymic.net_run package
  • pymic.transform package
  • pymic.util package

Citation

If you use PyMIC for your research, please acknowledge it accordingly by citing our paper:

G. Wang, X. Luo, R. Gu, S. Yang, Y. Qu, S. Zhai, Q. Zhao, K. Li, S. Zhang. PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation. Computer Methods and Programs in Biomedicine (CMPB). 231 (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 = {2022},
url = {https://doi.org/10.1016/j.cmpb.2023.107398},
journal = {Computer Methods and Programs in Biomedicine},
volume = {231},
pages = {107398},
}