no code implementations • 1 Nov 2020 • Dan Nguyen, Azar Sadeghnejad Barkousaraie, Gyanendra Bohara, Anjali Balagopal, Rafe McBeth, Mu-Han Lin, Steve Jiang
We propose to use Monte Carlo dropout (MCDO) and the bootstrap aggregation (bagging) technique on deep learning models to produce uncertainty estimations for radiation therapy dose prediction.
no code implementations • 19 Jun 2020 • Gyanendra Bohara, Azar Sadeghnejad Barkousaraie, Steve Jiang, Dan Nguyen
We studied and compared two different models, Model I and Model II.
no code implementations • 14 Apr 2020 • Azar Sadeghnejad-Barkousaraie, Gyanendra Bohara, Steve Jiang, Dan Nguyen
We propose a reinforcement learning strategy using Monte Carlo Tree Search capable of finding a superior beam orientation set and in less time than CG. We utilized a reinforcement learning structure involving a supervised learning network to guide Monte Carlo tree search (GTS) to explore the decision space of beam orientation selection problem.
no code implementations • 16 Aug 2019 • Dan Nguyen, Rafe McBeth, Azar Sadeghnejad Barkousaraie, Gyanendra Bohara, Chenyang Shen, Xun Jia, Steve Jiang
We propose a novel domain specific loss, which is a differentiable loss function based on the dose volume histogram, and combine it with an adversarial loss for the training of deep neural networks to generate Pareto optimal dose distributions.