1 citations · 1 across the 5 of their papers we have counts for
5 papers
Morphology signal in whole slide image foundation models can automatically triage slides
Ayushi Sinha, Shashank Yadav, Benjamin Holmes +9
Patient exams in the cancer diagnosis and staging process typically generate several whole slide images (WSIs). One of the initial steps in training models on WSI data is identifyi…
The Cost of Binarizing Survival Outcomes in Clinical Prognostic Modeling
Shashank Yadav, David M. Routman, Andrew Y. K. Foong
Survival analysis is an established framework for analyzing time-to-event data, yet many clinical machine learning studies still binarize the outcome before model training. This pr…
A Two-Stage Framework for Fast Proton Spot Map Generation in Pencil Beam Scanning Prostate SBRT Planning
Xueyan Tang, Hok Wan Chan Tseung, Mark Pepin +6
Background: In pencil beam scanning (PBS) proton therapy, plans are delivered as proton spot maps (PSMs). Although deep learning can rapidly predict 3D dose, direct conversion of d…
The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology
Jason Holmes, Federico Mastroleo, Mariana Borras-Osorio +17
Objective: To describe the design and early clinical evaluation of The Daily Dose (TDD), an LLM-driven, automated clinical summarization and clinical-trial identification system in…
RadOnc-GPT: An Autonomous LLM Agent for Real-Time Patient Outcomes Labeling at Scale
Jason Holmes, Yuexing Hao, Mariana Borras-Osorio +22
Manual labeling limits the scale, accuracy, and timeliness of patient outcomes research in radiation oncology. We present RadOnc-GPT, an autonomous large language model (LLM)-based…