To systematically assess whether commonly proposed architectural enhancements provide measurable benefits for deep learning-based radiotherapy dose prediction, using controlled comparisons of 3D U-Net variants to support evidence-based model selection and est...
Author profile
Hao Guo
Icahn School of Medicine at Mount Sinai
A Controlled Evaluation of Architectural Enhancements to 3D U-Net for Automated Radiotherapy Dose Prediction
Poster Program · Therapy Physics
Improving Portability of Knowledge-Based Planning Using an LLM-Driven Plan Refinement Framework
Knowledge-based planning (KBP) improves plan quality and efficiency. However, training institution-specific models requires substantial clinical data and expertise, and publicly available models may not align with local clinical objectives. This study evaluat...
Proffered Program · Therapy Physics
Teaching an LLM to Learn: A Self-Learning Approach for Autonomous Radiotherapy Planningcopilot for Locally Advanced Lung Cancer
To evaluate whether a Large Language Model (LLM)–driven autonomous planning system can self-learn planning strategies from human planner logs and apply this knowledge to generate clinically compatible radiotherapy plans without manual refinements.
Proffered Program · Therapy Physics