125 citations · 188 across the 7 of their papers we have counts for
7 papers
3D dose prediction for Gamma Knife radiosurgery using deep learning and data modification
Binghao Zhang, Aaron Babier, Timothy C. Y. Chan +1
Purpose: To develop a machine learning-based, 3D dose prediction methodology for Gamma Knife (GK) radiosurgery. The methodology accounts for cases involving targets of any number,…
OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
Aaron Babier, Rafid Mahmood, Binghao Zhang +56
We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients w…
OpenKBP: The open-access knowledge-based planning grand challenge
Aaron Babier, Binghao Zhang, Rafid Mahmood +4
The purpose of this work is to advance fair and consistent comparisons of dose prediction methods for knowledge-based planning (KBP) in radiation therapy research. We hosted OpenKB…
The importance of evaluating the complete automated knowledge-based planning pipeline
Aaron Babier, Rafid Mahmood, Andrea L. McNiven +2
We determine how prediction methods combine with optimization methods in two-stage knowledge-based planning (KBP) pipelines to produce radiation therapy treatment plans. We trained…
Knowledge-based automated planning with three-dimensional generative adversarial networks
Aaron Babier, Rafid Mahmood, Andrea L. McNiven +2
We develop a knowledge-based automated planning (KBAP) pipeline that generates treatment plans using deep neural network architectures for predicting 3D doses. Our pipeline consist…
Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks
Rafid Mahmood, Aaron Babier, Andrea McNiven +2
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…
Learning to Optimize Contextually Constrained Problems for Real-Time Decision-Generation
Aaron Babier, Timothy C. Y. Chan, Adam Diamant +1
The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities. In this work, we combine techniques f…