Dr. Amir Amini on Deep Learning in Medical Imaging Hosted by Purdue BME May 30, 2025 | 9:30am MJIS 2001 Zoom Link: https://purdue-edu.zoom.us/j/96980293910?pwd=Ib8oaJjLPPghPUOEIOSce53KC5krxA.1<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpurdue-edu.zoom.us%2Fj%2F96980293910%3Fpwd%3DIb8oaJjLPPghPUOEIOSce53KC5krxA.1&data=05%7C02%7Cbmeroundtable-list%40ecn.purdue.edu%7C8a53fcb87b224ddc351008dd947d76ae%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638829988764384558%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=fCEImXnYBtjFJNW2HRnXzLYxTpUZyL3kWkzCmSTP2Sk%3D&reserved=0> "Developing Deep Learning Models for Medical Imaging: Managing the Paucity and Training Data" [cid:image003.jpg@01DBC644.CC702920] Amir Amini, PhD Professor and Endowed Chair in Bioimaging University of Louisville Abstract: Medical imaging has advanced dramatically with the rise of artificial intelligence, enabling automated image interpretation at a level comparable to expert clinicians. However, deep learning models require large datasets for training, which are often unavailable in medical contexts due to cost, privacy and the rarity of certain conditions. In this talk, Dr. Amir Amini will present strategies to overcome these challenges including innovative machine learning techniques developed at the University of Louisville's Medical Imaging Lab. His work focuses on improving AI capabilities in diagnostic imaging, reducing dependence on invasive procedures and extracting physiologic data from underutilized sources - all while managing the limitations of sparse data environments. Bio: Dr. Amir Amini is the Endowed Chair in Bioimaging and Professor of Electrical and Computer Engineering at the University of Louisville. Prior to his current role he held faculty appointments at Yale University and Washington University in St. Louis. A recognized leader in medical image analysis, he has served in editorial and leadership roles with IEEE Transactions on Medical Imaging and multiple international conferences including SPIE Medical Imaging and the IEEE International Symposium on Biomedical Imaging. His research, funded by NIH, NSF and private industry, spans MRI-based motion and flow analysis and deep learning for diagnostic imaging. Dr. Amini was named a Fellow of the IEEE, AIMBE, SPIE and the International Academy of Medical and Biological Engineering. In 2025, he was appointed Editor-in-Chief of IEEE Transactions on Biomedical Engineering. His laboratory focuses on developing AI-driven analysis tools across a variety of imaging modalities with applications in computer-aided diagnosis and radiation therapy planning. His work also addresses challenges in extracting clinically meaningful data from imaging sequences that were previously inaccessible, helping reduce the need for invasive biopsies and improving patient outcomes. He received the Distinguished Alumni Award from the College of Engineering at UMass Amherst in 2020. ~ BME Faculty Host: Vitaliy Rayz ~ -- Bmeroundtable-list mailing list Bmeroundtable-list@ecn.purdue.edu https://engineering.purdue.edu/ECN/mailman/listinfo/bmeroundtable-list
