61 citations · 62 across the 6 of their papers we have counts for
6 papers
Dual-Based Weight Selection for Approximate Linear Programming
Su Li, Andre A. Cire, Adam Diamant +1
Approximate Linear Programming (ALP) is widely used for large-scale Markov Decision Processes (MDPs), but its performance can be sensitive to the choice of state-relevance weights,…
Prediction of Personal Protective Equipment Use in Hospitals During COVID-19
Eugene Furman, Alex Cressman, Saeha Shin +4
Demand for Personal Protective Equipment (PPE) such as surgical masks, gloves, and gowns has increased significantly since the onset of the COVID-19 pandemic. In hospital settings,…
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…