UMRAM Semineri: “Data driven interference in diffusion MRI: Deep learning harmonization of quantitative brain biomarkers”, Bennett A. Landman, 16:00 26 Nisan (EN)

“Data driven interference in diffusion MRI: Deep learning harmonization of quantitative brain biomarkers”

Professor Bennett A. Landman
Vanderbilt University

Date/Time: Tuesday, April 26th, 4:00 pm

Zoom Meeting ID: Please contact to the department

Abstract: Diffusion-weighted magnetic resonance imaging (DW-MRI) has been included in many national-scale studies. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency. I will discuss successful applications of deep learning with diffusion MRI and opportunities for deep learning innovation with diffusion MRI data.

About the Speaker: Bennett A. Landman, Ph.D. is a Professor and Department Chair of Electrical and Computer Engineering at Vanderbilt University, with appointments in Computer Science, Biomedical Engineering, Radiology and Radiological Sciences, Psychiatry and Behavioral Sciences, Biomedical Informatics, and Neurology. He graduated with a bachelor of science (’01) and master of engineering (’02) in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, MA. After graduation, he worked in an image processing startup company and a private medical imaging research firm before returning for a doctorate in biomedical engineering (‘08) from Johns Hopkins University School of Medicine, Baltimore, MD. From 2010 to 2021, he served in the Faculty of the Electrical Engineering and Computer Science Department, Vanderbilt University, Nashville, TN. In July 2021, he joined and became the first chair of the newly formed Electrical and Computer Engineering Department. His research concentrates on applying image-processing technologies to leverage large-scale imaging studies to improve understanding of individual anatomy and personalize medicine.

Dr. Landman has received grant funding from the National Institutes of Health, the National Science Foundation, the Department of Defense, and industry support. He is highly collaborative with 340+ co-authors across disciplines, career stages, and institutions, resulting in 340+ peer-reviewed publications and 9,500+ citations. He served on the MICCAI Society Challenge Working Group, as co-chair of the SPIE Medical Imaging Image Processing conference (2017-2021), as co-chair of the SIIM Machine Learning Tools Committee (2018-2021), and on the editorial boards of the IEEE Transactions of Medical Imaging (2015-) and SIIM Journal of Digital Imaging. He has organized 11 workshops and challenges at MICCAI since 2011 and has supported challenges with SPIE, ISBI, ISMRM, and Kaggle. He served as founding director of the Center for Computational Imaging at the Vanderbilt University Institute of Image Science and as chair of the faculty advisory board of the Vanderbilt University Advanced Computing Center for Research and Education (ACCRE). He is currently the Principal Scientist of ImageVU, Vanderbilt’s clinical data reuse initiative