Special Seminar: Yading Yuan, Associate Professor of Radiation Oncology
Guest Speaker: Yading Yuan, Associate Professor of Radiation Oncology (Physics), Columbia University Irving Medical Center
Chaired By: Clifford Stein, Interim Director of the Data Science Institute
This will be a HYBRID event. Please indicate on your registration if you will attend virtually or would like to be added to the in-person waitlist (due to limited seating).
- In-Person Location: 1401 Northwest Corner – 550 W 120th St, 14th Floor, New York, NY 10027
- Virtual: Zoom link to be sent upon registration
AI-Enhanced Radiation Therapy
Radiation therapy is a crucial pillar of cancer treatment and about 60% of cancer patients in the US will receive radiation therapy during their treatment. With the aim of precisely delivering radiation in the interest of optimizing cure while reducing side effects, radiation therapy has become increasingly complex over the past decades owning to the technological and procedural advances in the field, such as treatment planning with multi-modality medical imaging, real-time tumor motion tracking, image guidance in treatment, and adaptive treatment. This requires managing a large amount of heterogeneous information including clinical, imaging, dosimetric and biological data, along with human-machine interactions, optimizations, and decision-making processes. The growing complexity coupled with the increasing incidence of cancer not only resulted in the increasing variability of treatment quality, but also exacerbated the radiation oncology workforce shortage worldwide. Data-driven approaches, such as deep machine learning or AI, have potential to make radiation therapy more efficient, consistent, accessible, cost-effective and personalized. In this talk, I will present several applications of deep learning in radiation therapy and discuss the strategies to improve generalization of medical AI models through federated learning.
Bio: Yading Yuan, PhD. joined Columbia University Irving Medical Center as an Associate Professor of Radiation Oncology (Physics) in April 2023. He graduated with a PhD in medical physics from the University of Chicago in 2010 where he worked on developing machine learning algorithms for computer-aided breast cancer diagnosis with multi-modality medical imaging. After completing the clinical medical physics residency training from Harvard Medical Physics Residency Program in 2013, he joined the Mount Sinai Hospital as an Assistant Professor and clinical medical physicist in the Department of Radiation Oncology, where he was promoted to Associate Professor in 2020. His research lies in the interdisciplinary fields in computer engineering, physics and medical imaging, with primary focus on clinical and scientific innovation in radiation oncology and on translating recent technical advancements in data science and engineering into clinical practice to improve patient care. He has strong interest in research related to the follow aspects: 1) automated systems that learn clinical experts’ knowledge and skills in interpreting medical images for various clinical tasks such as tumor/organ contouring and knowledge-based treatment planning; 2) large-scale AI systems in cancer management; 3) data-driven medical image reconstruction algorithms and 4) panomics for personalized cancer treatment. He is a clinical medical physicist certified by American Board of Radiology and holds a New York State license for clinical therapeutic medical physics.