125 citations · 225 across the 6 of their papers we have counts for
7 papers
Cross-Task Attention Network: Improving Multi-Task Learning for Medical Imaging Applications
Sangwook Kim, Thomas G. Purdie, Chris McIntosh
Multi-task learning (MTL) is a powerful approach in deep learning that leverages the information from multiple tasks during training to improve model performance. In medical imagin…
Domain Adaptation of Automated Treatment Planning from Computed Tomography to Magnetic Resonance
Aly Khalifa, Jeff Winter, Inmaculada Navarro +2
Objective: Machine learning (ML) based radiation treatment (RT) planning addresses the iterative and time-consuming nature of conventional inverse planning. Given the rising import…
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…
Robust Direct Aperture Optimization for Radiation Therapy Treatment Planning
Danielle A. Ripsman, Thomas G. Purdie, Timothy C. Y. Chan +1
Intensity-modulated radiation therapy (IMRT) allows for the design of customized, highly-conformal treatments for cancer patients. Creating IMRT treatment plans, however, is a math…
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…
Fully Automated Treatment Planning for Head and Neck Radiotherapy using a Voxel-Based Dose Prediction and Dose Mimicking Method
Chris McIntosh, Mattea Welch, Andrea McNiven +2
Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient…