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Prof. Hayit Greenspan

Prof. Hayit Greenspan

Faculty of Engineering 

 Tel Aviv University

Bio:

Hayit Greenspan is a Professor of Biomedical Engineering in the Faculty of Engineering, Tel-Aviv University. She is also the Chief Scientist  of RADLogics Inc. Dr. Greenspan received the B.S. and M.S. degrees in Electrical Engineering (EE) from the Technion, and the Ph.D. degree in EE from CALTECH – California Institute of Technology. She was a Postdoc with the CS Division at U.C. Berkeley following which she joined Tel-Aviv University.  From 2008 until 2011, she was a visiting Professor at Stanford University, Department of Radiology, Faculty of Medicine. She was also a visiting researcher at IBM Research in the Multi-modal Mining for Healthcare group, in Almaden CA.

Dr. Greenspan has over 150 publications in leading international journals and conferences and has received several awards and patents. She is member of several journal and conference program committees, including SPIE medical imaging, IEEE_ISBI and MICCAI.  She serves as an Associate Editor for the IEEE Trans on Medical Imaging (TMI) journal.  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.

Title:

Deep Learning in Medical imaging: Solving the Data Augmentation Challenge for Enhanced-value Radiology Reporting

Abstract:

Medical image acquisition has improved substantially over recent years, with devices acquiring data at faster rates and increased resolution. The image interpretation process, however, has only recently begun to benefit from computer technology. Most interpretations of medical images are performed by radiologists; however, image interpretation by humans is limited due to  large variations across interpreters and fatigue. The Radiologist main tasks include an initial search process to detect abnormalities, segmentation to quantify measurements and characterization of findings into categories such as benign vs malignant.  

In this talk I will give an overview of the deep learning computer-aided detection and diagnosis tools we are developing, which can support the detection, segmentation and the characterization tasks. Examples will be presented in Chest Xray, CT liver ,and MRI brain analysis. Obtaining large-scale annotated datasets is a key challenge in the medical domain.  I will present novel methods we are developing to solve these data challenges. I will conclude with an overview of possible translations of these tools towards augmented radiology reports, and more efficient radiologist workflows.