Amir Amini

Professor, Endowed Chair of Bio-Imaging

Amir Amini, Ph.D., is Professor and Endowed Chair in Bioimaging in the ECE Dept. at the University of Louisville. He received B.S. in Electrical Engineering from the University of Massachusetts, Amherst with high honors when at 18 he was the youngest graduate of the University and Ph.D. in Electrical Engineering and Computer Science from University of Michigan Artificial Intelligence Laboratory. After postdoctoral work on biomedical imaging, he was on faculty at Yale as Assistant Professor. He then moved to Washington University in St. Louis where he was Assistant Professor and then Associate Professor with tenure. He has been at University of Louisville since August 2006 where he directs the Medical Imaging Laboratory and conducts research in the area of AI  and Deep Learning in Radiology and Radiation Therapy as well as in MRI of flow and motion.  Dr. Amini has been on the editorial board of IEEE Trans. On Medical Imaging since 1999 , Elsevier's journal of Computerized Medical Imaging and Graphics since 2012,  IEEE Trans. on Biomedical Engineering since 2014, IEEE Journal of Biomedical Health Informatics (2016-2019), the IEEE Open Journal of Engineering in Medicine and Biology since 2019, and IEEE Reviews in Biomedical Engineering since 2020. He has been on the scientific advisory board of IEEE Journal of Biomedical Health Informatics since 2020. Dr. Amini was elevated to grade of Fellow of the IEEE (Engineering in Medicine and Biology Society) in 2007  with the citation, ``for contributions to Cardiovascular Imaging and Medical Image Analysis.'' With more than 430,000 members in over 150 countries, IEEE (Institute of Electrical and Electronics Engineers) is the world's largest professional society dedicated to the advancement of technology. Only 0.1 percent of the active membership worldwide can be elevated to the rank of Fellow in any given year. In 2013, he received the Distinguished Lecturer Award from the IEEE Engineering in Medicine and Biology Society and later served on the IEEE EMBS Administrative Committee for the term 2016-2018. He co-chaired the IEEE International Symposium on Biomedical Imaging which took place in Washington, D.C. in April 2018 and co-chaired Symposium #1 of the IEEE EMBS Grand Challenges Forum on Data Science in Engineering and Health Care addressing medical imaging in February 2021.

Dr. Amini was elected to the College of Fellows of the American Institute of Medical and Biological Engineering  in 2017 and was elevated to Fellow of SPIE - the International Society for Optics, Photonics, and Imaging in 2019. He is also a Fellow of the Asia-Pacific Artificial Intelligence Association (AIAA).  Dr. Amini served as Vice President for Publications for the IEEE Engineering in Medicine and Biology Society for the term 2020-2021. In this role, he oversaw publication of 6 EMBS primary-sponsored journals and 8 co-sponsored journals - all in the field of Biomedical Engineering. His research has been funded by the National Science Foundation, the National Institutes of Health, private foundations, and industry.


  • B.S. in Electrical Engineering, University of Massachuetts - Amherst, 1983
  • MSE in Electrical Engineering, University of Michigan - Ann Arbor, 1984
  • Ph.D. in Electrical Engineering, University of Michigan - Ann Arbor, 1990


AI in Medical Imaging Informatics: Current Challenges and Future Directions- 2020

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.

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