Yading Yuan, PhD, DABR

  • Herbert and Florence Associate Professor of Radiation Oncology (Physics) (in the Data Science Institute) at CUMC

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Overview

Dr. Yading Yuan is an Associate Professor and the Director of Resident Program in Medical Physics (Therapy) in the Department of Radiation Oncology at the Columbia University Irving Medical Center. He also holds additional appointment in Columbia University Data Science Institute.

Dr. Yuan earned his PhD in medical physics from the University of Chicago, where he focused on developing machine-learning algorithms for computer-aided breast cancer diagnosis using multi-modal medical imaging. He completed his residency training in therapeutic medical physics at the Harvard Medical Physics Residency Program. Before joining Columbia University Irving Medical Center in April 2023, he served as an Associate Professor in the Department of Radiation Oncology at the Icahn School of Medicine at Mount Sinai, where he also directed the CAMPEP-accredited Medical Physics Residency Program.

Academic Appointments

  • Herbert and Florence Associate Professor of Radiation Oncology (Physics) (in the Data Science Institute) at CUMC

Administrative Titles

  • Director, Resident Program in Medical Physics (Therapy)

Languages

  • Chinese
  • English

Credentials & Experience

Education & Training

  • BEng, 1998 Engineering Physics, Tsinghua University, Beijing, China
  • PhD, 2010 Medical Physics, University of Chicago, Chicago, IL
  • Residency: 2013 Harvard University Medical School, Boston, MA

Committees, Societies, Councils

  • Member, American Association of Physicists in Medicine (AAPM)
  • Member, International Society of Optical Engineering (SPIE)
  • Member, American Society for Radiation Oncology (ASTRO)
  • Member, Institute of Electric and Electronics Engineers (IEEE)
  • Member, Radiological And Medical Physics Society (RAMPS)
  • Member, The Medical Image Computing and Computer Assisted Intervention Society (MICCAI)
  • Associate Editor: British Journal of Radiology (BJR) | Artificial Intelligence

Honors & Awards

  • 2021: NIH/NIBIB Trailblazer Award
  • 2021: 2nd place (1200+ teams), Automatic Brain Tumor Segmentation (BraTS) Challenge using multi-parametric MRI images sponsored by RSNA, MICCAI and American Society of Neuroradiology (ASNR)
  • 2021: Educational Merit Award, RSNA
  • 2018: SINAInnovation 4D Technology Development Award, Icahn School of Medicine at Mount Sinai
  • 2017: 1st place (386 participants), Automatic liver segmentation competition in 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2017 liver and tumor segmentation challenge
  • 2017: 1st place (593 registrations), Automatic lesion segmentation competition in IEEE International Symposium of Biomedical Imaging (ISBI) 2017 challenge on Skin Lesion Analysis Towards Melanoma Detection

Research

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. His research has been funded by the Department of Defense (DOD), the National Institutes of Health (NIH), and various industrial and charitable funding agencies.

Dr. Yuan has strong interests in research related to the following aspects:

  • Medical imaging and quantitative image analysis
  • Automated treatment planning for radiation therapy
  • Online replanning for adaptive radiation therapy
  • AI strategies for multi-modality information integration in cancer diagnosis, personalized treatment, prognosis and outcome assessment
  • Scalable, privacy-preserved and trustworthy AI systems for healthcare

Research Interests

  • Adaptive Radiotherapy
  • Artificial Intelligence (AI)
  • Automated Treatment Planning
  • Clinical Outcome Assessment
  • Medical Imaging
  • Multimodal Cancer Therapy
  • Personalized Medicine
  • Quantitative Image Analysis

Selected Publications

  1. M. Asad and Y. Yuan*, “FeSEC: a secure and efficient federated learning framework for medical imaging. SPIE Medical Imaging Conference 2024: Imaging Informatics for Healthcare, Research, and Applications, vol 12931, pp. 85-92, 2024
  2. J. Chen and Y. Yuan*, “Decentralized gossip mutual learning (GML) for automatic head and neck tumor segmentation”, SPIE Medical Imaging Conference 2024: Computer-aided diagnosis: vol 12927, 129270T, 2024
  3. P. Bilic et al.” The liver segmentation benchmark (LiTS)”, Medical Image Analysis, 84, 102680, 2023 [My contribution: the 1st place in liver segmentation, the 5th place in liver tumor segmentation and the 4th place in tumor burden estimation]
  4. S. Pati et al. “Federated learning enables big data for rare cancer boundary detection”, Nature Communications, 13(1), 7346, (2022)
  5. Y. Yuan,” Evaluating scale attention network for automatic brain tumor segmentation with a large multiple-parametric MRI database,” In BrainLesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Lecture Notes in Computer Science, vol 12963, pp. 42-53, Springer, Cham, (2022)
  6. Y. Yuan, R. Sheu, L. Fu and Y-C Lo, “A deep regression model for seed identification in prostate brachytherapy,” In Proceedings of MICCAI 2019, LNCS vol. 11768, Springer, Cham: pp385-393 (2019)

For a complete list of publications, please visit PubMed.gov