Artificial Intelligence for Medical Image Analysis
SangHyun Park received the B.S. degree in Department of electrical and electronic engineering from Yonsei University, Seoul, South Korea, in 2008, and the Ph.D. degree in Department of electrical and computer engineering from Seoul National University, Seoul, in 2014. From 2014 to 2016, he was a Postdoctoral Fellow with the Image Display, Enhancement, and Analysis Laboratory, Department of Radiology, The University of North Carolina, Chapel Hill, NC, USA. From 2016 to 2017, he was a Postdoctoral Fellow with the SRI International, Menlo Park, CA, USA. Since 2017, he has been an Assistant professor with the Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea. His research interests include medical image analysis, computer vision, and machine learning.
Recently, deep learning has achieved success in various applications for computer vision and image processing. Despite its success, it is often difficult to make a robust model for medical image analysis since constructing a large-scale dataset incurs a high cost in scanning, and manually creating annotations for volumetric images is laborious and time-consuming. Moreover, each hospital acquires images with different resolutions and modalities given that medical experts are interested in different tasks. In this lecture, I will introduce challenging problems for medical image analysis and the latest artificial intelligence methods to address them.
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