There is a significant interest in applying deep learning based solutions to problems in medicine and healthcare. This talk will focus on identifying actionable medical problems, and then recasting them as tractable deep learning problems and the techniques to solve them. The talk will have a technical focus on computer vision techniques, and their application to problems in radiological computer assisted diagnosis, clinical decision support in the intensive care unit. The talk will also discuss the unique nature of medical data and how it is sampled (non-randomly), as well as techniques for accelerating the training and generalization of models in a medical work environment.
Eric Karl Oermann, M.D. is an Instructor of Neurological Surgery in the Mount Sinai Health System and the Director of AISINAI, Mount Sinai’s artificial intelligence research group. He studied mathematics at Georgetown University with a focus on differential geometry. Prior to attending medical school, Dr. Oermann spent six months with the President’s Council on Bioethics studying human dignity under the mentorship of physician-philosopher Edmund Pellegrino. Dr. Oermann has won numerous awards for his scholarship including fellowships from the American Brain Tumor Association and Doris Duke Charitable Research Foundation where he was first exposed to neural networks and deep learning. He has published over fifty manuscripts spanning basic research on machine learning, tumor genetics, and the philosophy of medicine. As a PGY-2, Dr. Oermann was selected as one of Forbes Magazine’s 30 Under 30 for his work in applying machine learning to develop prognostic models for cancer patients. Dr. Oermann completed a postdoctoral fellowship at Google (Google Health / Verily Life Sciences). He is interested in weakly supervised learning, reinforcement learning with imperfect information, and in building artificial neural networks that more accurately model biological neural networks. As an actively practicing neurosurgeon, he is also interested in the application of deep learning to solve a wide range of problems in the medical sciences and improving clinical care.
Katie Link is a Phi Beta Kappa graduate of Johns Hopkins University with degrees in neuroscience and computer science. She is a FlexMed member of the Icahn School of Medicine Class of 2023. She is a member of the Mount Sinai Health System AI Consortium (AISINAI), and is passionate about applying her skills in machine learning to solving problems in healthcare. Currently she is a data analyst at the Allen Institute for Brain Science where she is working on building deep learning tools to solve practical problems in neuroscience research. As a member of AISINAI, her research has focused on developing a novel semi-supervised learning approach towards accelerating the training of deep convolutional neural networks.
Help us make this conference the best it can be for you. Have questions you'd like this speaker to address? Suggestions for issues that deserve extra attention? Feedback that you'd like to share with the speaker and other attendees?
Join the conversation here (requires login)
©2019, O'Reilly Media, Inc. • (800) 889-8969 or (707) 827-7019 • Monday-Friday 7:30am-5pm PT • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. • email@example.com