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Applications in Deep Learning in Imaging and Therapy

B Erickson1*, B Ibragimov2*, M Giger3*, (1) Mayo Clinic, Rochester, Mn, ,(2) Stanford University, Stanford, CA, (3) University of Chicago, Chicago, IL


(Tuesday, 7/31/2018) 4:30 PM - 6:00 PM

Room: Karl Dean Ballroom A1

AI and deep learning consist of agents or computer algorithms that learn from past experience to perform predictions or classifications. The tremendous possibilities that deep learning can bring to medical research and practice have triggered a flood of activities. While we are still years away from a prime time of AI-powered practice, it is apparent that a significant portion of future clinical innovations will be AI and deep learning-driven. In this session we will present a broad overview of deep learning and is applications in different aspects of medical physics, radiology and radiation oncology, such as deep learning-driven image analysis, cancer characterization, treatment planning, and assessment of radiation therapy response. Clinical, technical and ethical challenges will also be discussed.

Funding Support, Disclosures, and Conflict of Interest: Research is supported in parts by the NIH Quantitative Imaging Network (QIN) grant U01CA195564. MLG is a stockholder in R2 technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. She is a cofounder, equity holder, and scientific advisor for Quantitative Insights.



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