Hayit Greenspan, PhD

Dr. Greenspan is co-director of the Artificial Intelligence and Emerging Technologies in Medicine (AIET) PhD concentration at the Graduate School of Biomedical Sciences at the Icahn School of Medicine at Mount Sinai in New York. In 2021, she was appointed to be the Director of AI in Imaging at BMEII and the Director of AI Engineering Core at BMEII. In this role, she will focus on developing leading AI solutions for medical imaging applications, form collaborative efforts of the engineering and the clinical needs and lead the development of educational efforts in AI for medical applications. Dr.  Greenspan holds academic appointments as Professor of Radiology at the BioMedical Engineering and Imaging Institute (BMEII) at the Icahn School of Medicine at Mount Sinai and of Biomedical Engineering at Tel-Aviv University. Dr. Greenspan is also a Co-Founder and Chief Scientist of RADLogics Inc. – a company that focuses on bringing newly developed AI image analysis tools to radiologists for clinical use.   

Dr. Greenspan received her master’s degree in Electrical Engineering from the Technion-Israel Institute of Technology. She earned her doctorate in Electrical Engineering from California Institute of Technology and completed a postdoc with the computer science division at the University of California-Berkeley. She was a visiting professor at Stanford University’s Department of Radiology and at the Multimodal Mining Group at IBM Research, Almaden, CA. Dr. Greenspan has over 200 publications in leading international journals and conferences (h-index 53) and has received several awards and patents. She is a member of journal and conference program committees, including SPIE medical imaging, IEEE ISBI and MICCAI.  She served as an Associate Editor for the IEEE Transactions on Medical Imaging (TMI) journal. Recently she was the Program Chair for IEEE ISBI 2020. In 2016 she was the Lead Co-editor for a special issue on Deep Learning in Medical Imaging in IEEE TMI. In 2017 she co-edited an Elsevier Academic Press book on Deep learning for Medical Image Analysis and is a co-editor of the second edition of the book.

 

Research

I develop new Deep-Learning (DL) based methodologies applied to varying tasks in the medical image analysis space. The focus is on technology development, along with applications to real clinical needs. Tasks include MRI brain image analysis, CT liver analysis, Xray pathology detection and many more. In the past year, I have been focusing on developing DL solutions in Xray and CT to support COVID-19 analysis: from detection, to characterization and treatment support.

 

Selected set of Publications

• S. K. Zhou,  H. Greenspan et al., "A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises," inProceedings of the IEEE, vol. 109, no. 5, pp. 820-838, May 2021,
doi: 10.1109/JPROC.2021.3054390.
• H. Greenspan, R. San Jose Estepar, WJ Niessen, E. Siegel, M. Nielsen. “Position paper on COVID-19 imaging and AI: From the clinical needs and technological challenges to initial AI solutions at the lab and national level towards a new era for AI in healthcare”. Med Image Anal. 2020;  
• M. Frid-Adar, R. Amer, O. Gozes, J. Nassar and H. Greenspan, "COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring," in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 6, pp. 1892-1903, June 2021,
• O. Gozes, M. Frid-Adar, H. Greenspan, PD. Browning, H. Zhang, W. Ji, A. Bernheim, E. Siegel. “Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis”. arXiv:200305037v3. 2020.
• M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, H. Greenspan, “GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification,” Neurocomputing, Vol. 321, pp. 321-331, 2018.
• A. Ben-Cohen, E. Klang,  I. Diamant, N. Rozendorn, SP Raskin, E. Konen, NM Amitai, H. Greenspan, “CT Image-based Decision Support System for Categorization of Liver Metastases Into Primary Cancer Sites: Initial Results”. Acad Radiol. 2017;24(12):1501-9. Epub 2017/08/06.
doi: 10.1016/j.acra.2017.06.008. PubMed PMID: 28778512.
• H. Greenspan, B. van Ginneken and R. M. Summers, "Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique," in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1153-1159, May 2016, doi: 10.1109/TMI.2016.2553401.