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), 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.

Install PyMIC

The released version of PyMIC (v0.4.0) is required for these examples, and it can be installed by:

pip install PYMIC==0.4.0

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
Classification	CHNCXR	Finetuning restnet18 and vgg16 for normal/tuberculosis X-ray image classification
Fully supervised segmentation	JSRT	Using a 2D UNet for lung segmentation from chest X-ray images
Fully supervised segmentation	JSRT2	Using a customized network and loss function for the JSRT dataset
Fully supervised segmentation	Fetal_HC	Using a 2D UNet for fetal head segmentation from 2D ultrasound images
Fully supervised segmentation	Prostate	Using a 3D UNet for prostate segmentation from 3D MRI
Semi-supervised segmentation	seg_ssl/ACDC	Semi-supervised methods for heart structure segmentation using 2D CNNs
Semi-supervised segmentation	seg_ssl/AtriaSeg	Semi-supervised methods for left atrial segmentation using 3D CNNs
Weakly-supervised segmentation	seg_wsl/ACDC	Segmentation of heart structure with scrible annotations
Noisy label learning	seg_nll/JSRT	Comparing different NLL methods for learning from noisy labels

Useful links

• PyMIC on Github: https://github.com/HiLab-git/PyMIC
• Usage of PyMIC: https://pymic.readthedocs.io/en/latest/usage.html