Advanced Lecture Series in Pattern Recognition
题 目 (TITLE)：Intelligent Medical Imaging at Scale: Insights from Applying Deep Learning
讲 座 人 (SPEAKER)：Dr. Michael Muelly, MDGoogle Research (Medical Brain), Stanford University, USA
主 持 人 (CHAIR)： Prof. Chenglin Liu
时 间 (TIME)：16:00pm, May 29 (Tuesday), 2018
地 点 (VENUE)：No.1 Conference Room (3rd floor), Intelligence Building
Artificial intelligence and cloud computing is changing the way that imaging data is handled and creating novel opportunities to leverage large scale data aggregation to train machine learning models which may enable the ability to expand access to care, enhance provider workflows, and increase patient safety. Google has been a pioneer in the machine learning space and has been heavily investing in research to apply those technologies to healthcare. In this talk, we present some of our research efforts and results spanning ophthalmology, pathology and radiology. We also review how the cloud will allow new insights from large patient populations and practical deployment of the resulting tools. When properly executed, the integration of deep-learning techniques with medical imaging will have tremendous impact in transforming healthcare through increasing access and ensuring quality for patients worldwide.
Dr. Muelly's work focuses on applying deep learning methodologies to radiology and an array of non-clinical predictions within healthcare. He also is very interested in real-word deployment strategies for AI tools in healthcare and solving challenges to unlock the power of data for patients. Prior to joining Google, Dr. Muelly was a full-time radiologist and researcher at Stanford University. He continues on the radiology faculty at Stanford University and practices both diagnostic radiology and interventions using MR-guided focused ultrasound. He co-founded medical conversation chatbot startup GYANT, cloud-based PACS startup ClariPACS, and a network security solutions provider in the past. Dr. Muelly completed residency in diagnostic radiology and his subspecialty fellowship in body MRI at Stanford University. Prior to that he completed an internship in General Surgery at Penn State Hershey Medical Center, obtained his M.D. and attended graduate school in Engineering Physics at the Pennsylvania State University, and completed his B.S. in Mathematics and Computer Science at the University of Pittsburgh.