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ESE Seminar: “Foundations of Deep Learning and Applications in Medicine”
October 23, 2019 at 12:00 PM - 1:00 PM
Abstract: Recent advances in machine learning, computer vision, natural language processing, and robotics, offer a tremendous opportunity to transform medicine, from reactive and hospital centered to proactive and patient-centered. To fulfill this promise, new methods need to be developed which are more interpretable, can handle data at multiple spatial and temporal scales, and are robust to data heterogeneity and limited amounts of annotations. The first part of this talk will overview our recent work on the theory of deep learning, including sufficient conditions to guarantee that local minima are globally optimal, as well as an analysis of the optimization and regularization properties of dropout. The second part of this talk will overview our recent work on the development of methods for interpreting biomedical datasets arising in blood cell analysis, regenerative medicine, digital pathology, brain imaging, surgery and rehabilitation therapy.
Rene Vidal
Professor of Biomedical Engineering, The Johns Hopkins University
Rene Vidal is the Herschel Seder Professor of Biomedical Engineering and the Inaugural Director of the Mathematical Institute for Data Science at The Johns Hopkins University. His current research focuses on the foundations of deep learning and its applications in computer vision and biomedical data science. Dr. Vidal’s lab has pioneered the development of methods for dimensionality reduction and clustering, such as Generalized Principal Component Analysis and Sparse Subspace Clustering, and their applications to face recognition, object recognition, motion segmentation and action recognition. His lab has also created new technologies for a variety of biomedical applications, including detection, classification and tracking of blood cells in holographic images, classification of embryonic cardio-myocytes in optical images, and assessment of surgical skill in surgical videos. Dr. Vidal is or has been Associate Editor in Chief of TPAMI and CVIU, Program Chair of ICPR, ICCV and CVPR, co-author of the book “Generalized Principal Component Analysis” (2016), and co-author of more than 250 articles in machine learning, computer vision, biomedical image analysis, hybrid systems, robotics and signal processing. He is an AIMBE Fellow, an IEEE Fellow, an IAPR Fellow, a Sloan Fellow, and has received numerous awards for his work, including the 2017 D’Alembert Faculty Award, 2012 J.K. Aggarwal Prize, 2009 ONR Young Investigator Award, 2004 CAREER Award as well as best paper awards in machine learning, computer vision, controls, and medical robotics.