/DAR

Code for Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction (IEEE-TMI 2022)

Primary LanguagePythonMIT LicenseMIT

Learning from ambiguous labels for lung nodule malignancy prediction

This repo contains the official implementation of our paper: Learning from ambiguous labels for lung nodule malignancy prediction, which proposes a multi-view 'divide-and-rule' (MV-DAR) model to learn from both reliable and ambiguous annotations for lung nodule malignancy prediction on chest CT scans. The implementation of DAR model is released.

Paper on IEEE

Paper on arXiv

Requirements

This repo was tested with Ubuntu 20.04.4 LTS, Python 3.8, PyTorch 1.9.0, and CUDA 10.1. We suggest using virtual env to configure the experimental environment.

  1. Clone this repo:
git clone https://github.com/Merrical/DAR.git
  1. Create experimental environment using virtual env:
virtualenv .env --python=3.8 # create
source .env/bin/activate # activate
pip install -r requirements.txt

Bibtex

@article{liao2022learning,
  title={Learning from ambiguous labels for lung nodule malignancy prediction},
  author={Liao, Zehui and Xie, Yutong and Hu, Shishuai and Xia, Yong},
  journal={IEEE Transactions on Medical Imaging},
  year={2022},
  publisher={IEEE}
}

Contact Us

If you have any questions, please contact us ( merrical@mail.nwpu.edu.cn ).