This is an implementation of the Weakly-Supervised Segmentation for DiseaseLocalization in Chest X-Ray Images by Ostap Viniavskiy, Maria Dobko, and Oles Dobosevych.
We proposed a three-step approach to weakly-supervised semantic segmentation, which uses only image-level labels as supervision. The method can be applied to various medical problems as well as on datasets of different nature. We demonstrated the efficiency of this approach for semantic segmentation of Pneumothorax on chest X-ray images (SIIM-ACR Pneumothorax). We also evaluated the performance on PASCAL VOC 2012 dataset.
WSMIS is released under the GNU General Public License version 3 license.
Some postprocessing techniques are applied in between the steps, please refer to the paper for details.
All necessary modules are in the requirements.txt
pip install -r requirements.txt
This code should be compatible with Python versions >= 3.5.
All the experiments were performed on four Nvidia Tesla K80 GPUs.
python train.py
The description of possible configurations for training is provided in model_training/CONFIGURATIONS.md
Will be added soon
@incollection{Viniavskyi2020,
doi = {10.1007/978-3-030-59137-3_23},
url = {https://doi.org/10.1007/978-3-030-59137-3_23},
year = {2020},
publisher = {Springer International Publishing},
pages = {249--259},
author = {Ostap Viniavskyi and Mariia Dobko and Oles Dobosevych},
title = {Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images},
booktitle = {Artificial Intelligence in Medicine}
}