@inproceedings{7dee9f28b6804507813a81ee05d7c028,
title = "Trainable multi-contrast windowing for liver CT segmentation",
abstract = "This study proposes a trainable multi-contrast windowing method in order to optimally choose contrast windows for deep learning-based CT segmentation. Existing contrast windowing methods use parameters predefined by radiologists or manufacturers. These predefined contrast windows, however, have not been proven to be optimal set for machine learning based approaches. We therefore propose a trainable multi-contrast windowing module which can be easily integrated into deep convolutional neural networks. For performance evaluation, we investigate the effects of the trainable multi-contrast windows by applying the proposed windowing modules to a deep learning based segmentation network measuring liver tumors. The results show significant performance improvement when the windowing parameters are trainable. The proposed method enhances the performance for medical image analyses compared to rule-based windowing methods.",
keywords = "Computational tomography, Computer-aided diagnosis, Deep learning, Machine learning, Medical application, Semantic segmentation",
author = "Jangho Kwon and Kihwan Choi",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020 ; Conference date: 19-02-2020 Through 22-02-2020",
year = "2020",
month = feb,
doi = "10.1109/BigComp48618.2020.00-80",
language = "English",
series = "Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "169--172",
editor = "Wookey Lee and Luonan Chen and Yang-Sae Moon and Julien Bourgeois and Mehdi Bennis and Yu-Feng Li and Young-Guk Ha and Hyuk-Yoon Kwon and Alfredo Cuzzocrea",
booktitle = "Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020",
}