Automated Treatment Planning in Radiation Therapy Using Generative Adversarial Networks
Abstract
Knowledge-based planning (KBP) is an automated approach to radiation therapy treatment planning that involves predicting desirable treatment plans before they are then corrected to deliverable ones. We propose a generative adversarial network (GAN) approach for predicting desirable 3D dose distributions that eschews the previous paradigms of site-specific feature engineering and predicting low-dimensional representations of the plan. Experiments on a dataset of oropharyngeal cancer patients show that our approach significantly outperforms previous methods on several clinical satisfaction criteria and similarity metrics.
Cite
Text
Mahmood et al. "Automated Treatment Planning in Radiation Therapy Using Generative Adversarial Networks." Proceedings of the 3rd Machine Learning for Healthcare Conference, 2018.Markdown
[Mahmood et al. "Automated Treatment Planning in Radiation Therapy Using Generative Adversarial Networks." Proceedings of the 3rd Machine Learning for Healthcare Conference, 2018.](https://mlanthology.org/mlhc/2018/mahmood2018mlhc-automated/)BibTeX
@inproceedings{mahmood2018mlhc-automated,
title = {{Automated Treatment Planning in Radiation Therapy Using Generative Adversarial Networks}},
author = {Mahmood, Rafid and Babier, Aaron and McNiven, Andrea and Diamant, Adam and Chan, Timothy C. Y.},
booktitle = {Proceedings of the 3rd Machine Learning for Healthcare Conference},
year = {2018},
pages = {484-499},
volume = {85},
url = {https://mlanthology.org/mlhc/2018/mahmood2018mlhc-automated/}
}