Invited talk
Speaker: Jun Seita, M.D., Ph.D.
(RIKEN Center for Integrative Medical Sciences, Laboratory for Integrative Genomics)
Title: Impact of Deep Learning on Smart Healthcare
Abstract:
Due to the rapid development of deep learning in the last decade, the application of artificial intelligence to medical biology has become a highly anticipated field. In this talk, I would like to first reconfirm the definitions of "artificial intelligence," "machine learning," and "deep learning" to understand what they can do, what they cannot do, and what they need to do. Next, I would like to introduce some of our deep learning research such as future prediction from time-series medical data using recurrent neural networks, and learning of disease concepts using adversarial generative networks. Based on the above, we would like to discuss how artificial intelligence and humans can collaborate to update medicine and healthcare for the smart society.
Biography:
Jun Seita is Team Leader at RIKEN, Japan, where he leads 3 laboratories; Laboratory for Integrative Genomics, Center for Integrative Medical Sciences; Medical Data Deep Learning Team, Advanced Data Science Project; and Medical Data Sharing Unit, RIKEN Information R&D and Strategy Headquarters. He received his M.D. from the University of Tsukuba in 1996 and after completion of residency in cardiovascular surgery, he studied stem cell biology and received his Ph.D. in Medicine from the University of Tokyo. From 2006 to 2016, he had been in Stanford University studying single cell biology, systems biology, data-driven science, and machine learning. His research interests cover the areas where AI meets biology, medicine, and beyond. He is also professor at the University of Tsukuba, and fellow at avatarin inc.