CustomNet.py contains the description of the best system mentioned in the paper.
If you find this work helpful to you, please kindly cite:
@inproceedings{duanmu2020prediction,
title={Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data},
author={Duanmu, Hongyi and Huang, Pauline Boning and Brahmavar, Srinidhi and Lin, Stephanie and Ren, Thomas and Kong, Jun and Wang, Fusheng and Duong, Tim Q},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={242--252},
year={2020},
organization={Springer}
}
This repository is the official implementation of the paper 'Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data' published in the MICCAI 2020.
This tool is available under the GNU General Public License (GPL) (https://www.gnu.org/licenses/gpl-3.0.en.html) and the LGPL (https://www.gnu.org/licenses/lgpl-3.0.en.html).