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Deep Learning in Brain Quantification and Cancer Radiotherapy

Release time:October 26, 2018

Topic: Deep Learning in Brain Quantification and Cancer Radiotherapy

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

Time: 15:30-17:00, October27

Venue: IRC 308

Abstract:

This talk will introduce some of our recent deep learning work in MICCAI 2018. Specifically, for automatic quantification of early brain development in the first year of life, i.e., with the goal of early identification of brain diseases such as autism, deep learning based brain image segmentation and cortical surface parcellation have been developed. For early diagnosis of Alzheimer’s Disease (AD) with the goal of possible early treatment, deep learning has been applied to unsupervised brain registration for precise inter-subject comparison, distinctive-regions based disease diagnosis, and resting-state fMRI based early MCI (Mild Cognitive Impairment) diagnosis. Besides, for effective treatment of prostate cancer, especially for MRI-based cancer treatment, a novel context-aware GAN (Generative Adversarial Networks) has been developed for synthesizing CT from MRI. Also, two novel deep learning techniques have been developed for automatic and precise segmentation of pelvic organs from planning CT images to better guide radiotherapy. Both clinical significance of each medical problem and the motivation of each developed technique will be clarified in this talk.

Biography of the Speaker:

Dinggang Shen is Jeffrey Houpt Distinguished Investigator, and a Professor of Radiology, Biomedical Research Imaging Center (BRIC), Computer Science, and Biomedical Engineering in the University of North Carolina at Chapel Hill (UNC-CH). He is currently directing the Center for Image Analysis and Informatics, the Image Display, Enhancement, and Analysis (IDEA) Lab in the Department of Radiology, and also the medical image analysis core in the BRIC. He was a tenure-track assistant professor in the University of Pennsylvanian (UPenn), and a faculty member in the Johns Hopkins University. Dr. Shen’s research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 800 papers in the international journals and conference proceedings, with H-index 83. He serves as an editorial board member for eight international journals. He has also served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015. He will be General Chair for MICCAI 2019. He is Fellow of IEEE, Fellow of The American Institute for Medical and Biological Engineering (AIMBE), and Fellow of The International Association for Pattern Recognition (IAPR).

 

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