Machine Learning for Medical Image Processing
|Event Date:||March 31, 2017|
|Speaker:||Dong Hye Ye|
|Speaker Affiliation:||Research Scientist
|Type:||CNSIP Research Area Seminar
|Contact Name:||Professor Ed Delp
|School or Program:||Electrical and Computer Engineering
Medical image processing is essential for clinical diagnosis by providing quantitative visualization and analysis of underlying anatomy. Typically, medical image processing is done with statistical modeling which may not be sufficient to describe the complex variation of underlying anatomy. To tackle this challenge, I unlock the valuable prior knowledge from large image databases via machine learning techniques and use it to improve medical image processing. In this talk, I will present how machine learning can help medical image processing such as disease classification, CT reconstruction, and microscopy imaging.
Dr. Dong Hye Ye is a Research Scientist in Electrical and Computer Engineering at Purdue University. His research interests are in advancing image processing via machine learning. His publications have been awarded Best Paper at MICCAI-MedIA 2010 and Best Paper Runner-Up at ICIP 2015. During his PhD, Dong Hye conducted research at Section of Biomedical Image Analysis (SBIA) in Hospital of the University of Pennsylvania (HUP) and Microsoft Research Cambridge (MSRC). He received Bachelor's degree from Seoul National University in 2007 and Master's degree from Georgia Institute of Technology in 2008.