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Deep Learning Methods for Abnormality Detection and Segmentation in Computed Tomography and Magnetic Resonance Images

Dr. Fatemeh Zabihollahy

Postdoctoral Researcher and Instructor, The Johns Hopkins University

October 1, 2020 14:30 - 15:30

This is an online event. The details on how to join the event will be available once you register.

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abstract

Medical imaging, (e.g., computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), mammography, ultrasound, X-ray) has advanced at a rapid speed over last decades. Currently, the medical image interpretation is mostly performed by human experts, which is a tedious task and subject to high inter-operator variability. Deep learning is providing exciting solutions for automated medical image analysis problems. Recent advances in deep learning have helped to identify, classify, and quantify patterns in medical images. In this seminar, the principles and methods of deep learning concepts, particularly convolutional neural network (CNN) will be introduced. I will describe several interesting applications of deep learning for medical image analysis, including my recent works on segmenting myocardial scar (injured) tissue in the heart, prostate tumor detection, and kidney lesion localization in 3D MRI and CT images.

biography

Fatemeh Zabihollahy is currently a postdoctoral research fellow at The Johns Hopkins University. She received her bachelor degree in Biomedical Engineering from Shahid Beheshti University, Tehran, Iran in 2001, her Master of Applied Science in Biomedical Engineering in 2016, and her Ph.D in Electrical and Computer Engineering in 2020 both from Carleton University, Ottawa, Canada. Fatemeh is the receipant of Carleton Medal for her outstanding graduate work at the PhD level. She worked in the medical devices industry as an R&D engineer for ten years. Her research interest is in the field of application of deep learning techniques for medical image analysis.