Advances in medical imaging and artificial intelligence have created new opportunities for disease detection and characterization, clinical decision support, and quantitative assessment of physiological function. However, the development of reliable deep learning methods remains challenging, particularly in medical imaging applications where the availability of annotated training data is often limited.
In this talk, I will discuss selected research projects from my laboratory that address these challenges through different approaches to deep learning model development. Examples will include image enhancement and quantitative hemodynamic assessment using 4D flow MRI, as well as disease assessment from thoracic CT and other ongoing imaging applications. These projects will illustrate strategies for developing deep learning methods with limited training data, while highlighting methodological considerations, practical limitations, and opportunities for advancing AI-based medical imaging.
Amir A. Amini received his Ph.D. in Electrical Engineering and Computer Science from the University of Michigan. He is currently Endowed Chair in Bioimaging and Professor of Electrical and Computer Engineering at the University of Louisville. His previous faculty appointments were at Yale University and Washington University in St. Louis. He has held numerous leadership roles in medical imaging and image-analysis conferences, serving as a scientific program committee member, program chair, and conference chair. He was Symposium Co-Chair of SPIE Medical Imaging in 2007 and the IEEE International Symposium on Biomedical Imaging in 2018. He has served as an Associate Editor for IEEE Transactions on Medical Imaging, IEEE Transactions on Biomedical Engineering, IEEE Reviews in Biomedical Engineering, IEEE Open Journal of Engineering in Medicine and Biology, and Computerized Medical Imaging and Graphics. He served as Vice President for Publications of the IEEE Engineering in Medicine and Biology Society from 2020 to 2021. He became Editor-in-Chief of IEEE Transactions on Biomedical Engineering in 2025.
