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‎README.md

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PyMIC is a pytorch-based toolkit for medical image computing with annotation-efficient deep learning. Despite that pytorch is a fantastic platform for deep learning, using it for medical image computing is not straightforward as medical images are often with high dimension and large volume, multiple modalities and difficulies in annotating. This toolkit is developed to facilitate medical image computing researchers so that training and testing deep learning models become easier. It is very friendly to researchers who are new to this area. Even without writing any code, you can use PyMIC commands to train and test a model by simply editing configuration files. PyMIC is developed to support learning with imperfect labels, including semi-supervised and weakly supervised learning, and learning with noisy annotations.
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Currently PyMIC supports 2D/3D medical image classification and segmentation, and it is still under development. It was originally developed for COVID-19 pneumonia lesion segmentation from CT images. If you use this toolkit, please cite the following paper:
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Currently PyMIC supports 2D/3D medical image classification and segmentation, and it is still under development. If you use this toolkit, please cite the following paper:
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* G. Wang, X. Liu, C. Li, Z. Xu, J. Ruan, H. Zhu, T. Meng, K. Li, N. Huang, S. Zhang.
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[A Noise-robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions from CT Images.][tmi2020] IEEE Transactions on Medical Imaging. 39(8):2653-2663, 2020. DOI: [10.1109/TMI.2020.3000314][tmi2020]
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* G. Wang, X. Luo, R. Gu, S. Yang, Y. Qu, S. Zhai, Q. Zhao, K. Li, S. Zhang. (2022).
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[PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation.][arxiv2022] arXiv, 2208.09350.
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[tmi2020]:https://ieeexplore.ieee.org/document/9109297
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[arxiv2022]:http://arxiv.org/abs/2208.09350
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BibTeX entry:
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@article{Wang2022pymic,
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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},
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title = {{PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation}},
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year = {2022},
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url = {http://arxiv.org/abs/2208.09350},
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journal = {arXiv},
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volume = {2208.09350},
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pages = {1-10},
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}
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# Features
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PyMIC provides flixible modules for medical image computing tasks including classification and segmentation. It currently provides the following functions:

‎docs/source/api.rst

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API
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===
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.. autosummary::
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:toctree: generated
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.. toctree::
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:maxdepth: 4
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lumache
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pymic.io
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pymic.layer
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pymic.loss
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pymic.net
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pymic.net_run
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pymic.net_run_nll
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pymic.net_run_ssl
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pymic.net_run_wsl
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pymic.transform
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pymic.util

‎docs/source/conf.py

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# Configuration file for the Sphinx documentation builder.
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# -- Project information
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import os
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import sys
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sys.path.insert(0, os.path.abspath('./../..'))
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project = 'PyMIC'
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copyright = '2021, HiLab'

‎docs/source/index.rst

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Welcome to PyMIC's documentation!
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===================================
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PyMIC is a pytorch-based toolkit for medical image computing with annotation-efficient deep learning.
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PyMIC is developed to support learning with imperfect labels, including semi-supervised and weakly supervised learning, and learning with noisy annotations.
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PyMIC is a pytorch-based toolkit for medical image computing with annotation-efficient
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deep learning. PyMIC is developed to support learning with imperfect labels, including
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semi-supervised and weakly supervised learning, and learning with noisy annotations.
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Check out the :doc:`usage` section for further information, including
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how to :ref:`installation` the project.
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Check out the :doc:`installation` section for install PyMIC, and go to the :doc:`usage`
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section for understanding modules for the segmentation pipeline designed in PyMIC.
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Please follow `PyMIC_examples <https://github.com/HiLab-git/PyMIC_examples/>`_
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to quickly start with using PyMIC.
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.. note::
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This project is under active development. It will be updated later.
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Contents
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--------
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.. toctree::
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:maxdepth: 2
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:caption: Getting Started
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installation
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usage
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api
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Citation
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--------
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If you use PyMIC for your research, please acknowledge it accordingly by citing our paper:
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`G. Wang, X. Luo, R. Gu, S. Yang, Y. Qu, S. Zhai, Q. Zhao, K. Li, S. Zhang. (2022).
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PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation.
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arXiv, 2208.09350. <http://arxiv.org/abs/2208.09350>`_
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BibTeX entry:
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.. code-block:: none
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usage
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api
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@article{Wang2022pymic,
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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},
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title = {{PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation}},
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year = {2022},
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url = {http://arxiv.org/abs/2208.09350},
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journal = {arXiv},
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volume = {2208.09350},
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pages = {1-10},
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}

‎docs/source/installation.rst

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Installation
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============
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Install PyMIC using pip (e.g., within a `Python virtual environment <https://www.geeksforgeeks.org/python-virtual-environment/>`_):
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.. code-block:: bash
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pip install PYMIC
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Alternatively, you can download or clone the code from `GitHub <https://github.com/HiLab-git/PyMIC>`_ and install PyMIC by
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.. code-block:: bash
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git clone https://github.com/HiLab-git/PyMIC
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cd PyMIC
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python setup.py install
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PyMIC requires Python 3.6 (or higher) and depends on the following packages:
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- `pandas <https://pandas.pydata.org/>`_
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- `h5py <https://www.h5py.org/>`_
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- `NumPy <https://numpy.org/>`_
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- `scikit-image <https://scikit-image.org/>`_
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- `SciPy <https://www.scipy.org/>`_
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- `SimpleITK <https://simpleitk.org/>`_

‎docs/source/modules.rst

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pymic
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=====
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.. toctree::
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:maxdepth: 4
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pymic

‎docs/source/pymic.io.rst

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pymic.io package
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================
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Submodules
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----------
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pymic.io.h5\_dataset module
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---------------------------
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.. automodule:: pymic.io.h5_dataset
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:members:
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:undoc-members:
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:show-inheritance:
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pymic.io.image\_read\_write module
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----------------------------------
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.. automodule:: pymic.io.image_read_write
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:members:
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:undoc-members:
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:show-inheritance:
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pymic.io.nifty\_dataset module
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------------------------------
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.. automodule:: pymic.io.nifty_dataset
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:members:
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:undoc-members:
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:show-inheritance:
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Module contents
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---------------
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.. automodule:: pymic.io
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:members:
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:undoc-members:
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:show-inheritance:

‎docs/source/pymic.layer.rst

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pymic.layer package
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===================
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Submodules
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----------
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pymic.layer.activation module
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-----------------------------
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.. automodule:: pymic.layer.activation
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:members:
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:undoc-members:
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:show-inheritance:
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pymic.layer.convolution module
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------------------------------
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.. automodule:: pymic.layer.convolution
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:members:
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:undoc-members:
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:show-inheritance:
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pymic.layer.deconvolution module
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--------------------------------
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.. automodule:: pymic.layer.deconvolution
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:members:
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:undoc-members:
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:show-inheritance:
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pymic.layer.space2channel module
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--------------------------------
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.. automodule:: pymic.layer.space2channel
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:members:
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:undoc-members:
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:show-inheritance:
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Module contents
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---------------
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.. automodule:: pymic.layer
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:members:
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:undoc-members:
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:show-inheritance:

‎docs/source/pymic.loss.cls.rst

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pymic.loss.cls package
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======================
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Submodules
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----------
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pymic.loss.cls.basic module
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------------------------
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.. automodule:: pymic.loss.cls.basic
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:members:
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:undoc-members:
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:show-inheritance:
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pymic.loss.cls.util module
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--------------------------
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.. automodule:: pymic.loss.cls.util
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:members:
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:undoc-members:
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:show-inheritance:
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Module contents
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---------------
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.. automodule:: pymic.loss.cls
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:members:
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:undoc-members:
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:show-inheritance:

‎docs/source/pymic.loss.rst

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pymic.loss package
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==================
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Subpackages
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-----------
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.. toctree::
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:maxdepth: 4
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pymic.loss.cls
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pymic.loss.seg
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Submodules
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----------
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pymic.loss.loss\_dict\_cls module
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---------------------------------
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.. automodule:: pymic.loss.loss_dict_cls
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:members:
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:undoc-members:
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:show-inheritance:
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pymic.loss.loss\_dict\_seg module
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---------------------------------
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.. automodule:: pymic.loss.loss_dict_seg
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:members:
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:undoc-members:
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:show-inheritance:
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Module contents
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---------------
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.. automodule:: pymic.loss
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:members:
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:undoc-members:
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:show-inheritance:

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