Machine Learning in Radiology
In the last decade, machine learning (ML) has revolutionized a number of domains such as manufacturing, financial trading, and security. One of the domains in which ML has generated a lot of excitement is Healthcare. While to date, ML has demonstrated a great promise in a number of applications such as predictive healthcare and personalized medicine, in some other areas of healthcare, the application and influence of ML is still in doubt. In this talk, success and failure of the most recent ML applications in healthcare will be reviewed from both clinical and technical aspects. The main focus of the talk will be on ML in Radiology, as one of the most transforming areas in healthcare during the last few years. Attendees will learn about clinical challenges and technical opportunities in the field of Radiology.
Amir Tahmasebi is currently a project manager and senior research scientist in Clinical Informatics at Philips Cambridge Innovation Labs, Cambridge, MA, USA. He is leading a group of activities in the areas of Radiology and Oncology Informatics. His current research is focused on patient clinical context extraction and modeling, outcome analytics and clinical decision support. Dr. Tahmasebi also served as a project leader in Ultrasound Imaging and Intervention department contributing to a number of products including Uronav and PercuNav. Dr. Tahmasebi received his PhD degree in Computer Science from the School of Computing, Queen's University, Kingston, Canada. He is the recipient of the IEEE Best PhD Thesis award and Tanenbaum Post-doctoral Research Fellowship award. He is currently serving as an industrial Chair for IPCAI and IEEE ICHI 2018. Dr. Tahmasebi has published and presented his work in a number of conferences and journals including IEEE TMI, IEEE TBME, MICCAI, IPCAI, HBM, SPIE, SIIM, and RSNA. He has also been granted more than 10 patent awards.