Hai Pham

This study proposes a transformer-based deep learning framework for markerless lung tumor tracking that improves localization accuracy, robustness, and computational efficiency of real-time intrafraction motion management for seamless clinical integration.

Markerless lung tumor tracking has the potential to reduce target margins and improve organ-at-risk (OAR) sparing during radiotherapy. We previously proposed a deep learning–based target decomposition approach for real-time markerless lung tumor tracking. Thi...

Despite extensive research on automated treatment planning, manual trial-and-error optimization remains common in clinical practice. Knowledge-based and AI-driven approaches show promise but often lack robustness to evolving clinical protocols due to the need...

We present PortPy (Planning and Optimization for Radiation Therapy in Python), an open-source platform designed to accelerate research, benchmarking, and clinical translation of treatment planning optimization algorithms. PortPy provides curated benchmark dat...