Michael Kuo

Towards the Development of Clinically Relevant Applications of Deep learning in Healthcare

Medicine, by definition, is an information science that requires the capacity to actively acquire individualized and context-specific data, and to then iteratively evaluate, assimilate and refine this information against a vast database of medical knowledge in order to arrive at a small solution space and a corresponding set of implementable policies. Deep Learning, as a transformational tool, is thus extremely well suited to medical application; unfortunately, fundamental understanding of the domain and where and how Deep Learning can be applied in a clinically relevant manner still lag. In this talk I will share my group’s experience over the past decade with trying to develop more advanced and clinically relevant computational approaches in cancer that can integrate large and diverse multi-scale biological data sets including medical imaging, tissue, genomics, and clinical data, in order to predict an individual patient’s cancer genomics and likelihood of response to a particular therapy using only their medical imaging data. I will discuss how we are incorporating Deep Learning in our approaches and highlight other areas of future growth and opportunity in healthcare where Deep Learning can potentially have great impact.

Dr. Kuo received his Medical Degree from Baylor College of Medicine and did his clinical training in Diagnostic Radiology at Stanford University, where he also completed a clinical fellowship in Cardiovascular and Interventional Radiology. He served as Assistant Professor in the Department of Radiology at the University of California-San Diego from 2003-2009. In 2009 he moved to the University of California-Los Angeles where he is an Associate Professor in the Departments of Radiology, Pathology and Bioengineering and served as the Directors of both the Radiogenomics and Radiology-Pathology Programs. Dr. Kuo is an international leader in the field of Radiogenomics where he has published seminal foundational papers. His principle area of research focus is in the field of radiogenomics where his group applies integrative computational and biological approaches in order to derive actionable clinical insights and tools centered around patient stra tification and therapeutic response prediction by leveraging large multi-scale relational data sets including clinical outcomes, clinical imaging, tissue, cellular and subcellular biological data.

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