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BK21 PLUS Colloquium Announcement on Mar. 29 2018
W-Name Admin (IP: *.152.74.116) W-Date 2018-03-16 10:48 Hit 483

Title: Deep Learning for Medical Image Analysis

Speaker : Prof. Dinggang Shen (University of North Carolina at Chapel Hill)

Date: Mar. 29 (Thurs.), 2018

Time: 11:00 AM 

Location: Woo Jung Information & Communication bldg., Room 604

Hosted by Dept. of Brain & Cognitive Engineering, Korea University / Institute of Brain and Cognitive Engineering, Korea University / BK21+ Global Leader Development Division in Brain Engineering, Korea University / Interdisciplinary Major in Brain and Cognitive Science


This talk will discuss some of our recently developed deep learning methods for various neuroimaging applications. Specifically, 1) in neuroimaging analysis, we have developed an automatic brain measurement method for the first-year brain images with the goal of early detection of autism such as before 1-year-old. This effort is aligned with our recently awarded Baby Connectome Project (BCP) (where I serve as Co-PI), which will acquire MR images and behavioral assessments from typically developing children, from birth to five years of age. Besides, we have also developed a novel landmark-based deep learning method for early diagnosis of Alzheimer’s Disease (AD) with the goal of potential early treatment. 2) In image synthesis, we have developed a cascaded 3D CNN for reconstructing 7T-like MRI from 3T MRI for simultaneously enhancing image quality and tissue segmentation. Also, we have developed a novel Generative Adversarial Networks (GAN) based technique to estimate CT from MRI, for helping MRI-based cancer radiotherapy. All these techniques will be introduced in this talk, for the goal of early diagnosis of brain disorders.


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