Academic Lecture | Tensor Sparse Coding for Multi-Dimensional Medical Image Analysis


Published: 2019.11.21

Title: Tensor sparse coding for multi-dimensional medical image analysis

Time: 13:00 on November 27, 2019

Location:Information Building 133

Speaker:Yen-Wei Chen

Moderator: Li Qingli

Introduction of the speaker:

Yen-Wei Chen received the B.E. degree in 1985 from Kobe Univ., Kobe, Japan, the in 1987, and the D.E. degree in 1990, both from Osaka Univ., Osaka, Japan. He was a research fellow with the Institute for Laser Technology, Osaka, from 1991 to 1994. From Oct.1994 to Mar. 2004, he was an associate Professor and a professor with the Department of Electrical and Electronic Engineering, Univ. of the Ryukyus, Okinawa, Japan. He is currently a professor with the college of Information Science and Engineering, Ritsumeikan University, Japan. He is also an adjunct professor with the College of Computer Science, Zhejiang University, China and Zhejiang Lab, China. He was a visiting professor with the Oxford University, Oxford, UK in 2003 and a visiting professor with Pennsylvania State University, USA in 2010. His research interests include medical image analysis, computer vision and computational intelligence. He has published more than 300 research papers in a number of leading journals and leading conferences including IEEE Trans. Image Processing, IEEE Trans. SMC, Pattern Recognition. He has received many distinguished awards including ICPR2012 Best Scientific Paper Award, 2014 JAMIT Best Paper Award, Outstanding Chinese Oversea Scholar Fund of Chinese Academy of Science. He is/was a leader of numerous national and industrial research projects.

Lecture summary:

Due to the rapid development of imaging technologies, we have obtained a large amount of biomedical images. In addition to 3-dimensional spatial information, the biomedical images have temporal information. Efficient representation of the multi-dimensional biomedical image is an important issue for biomedical image analysis. Sparse coding is one of machine learning methods and is widely used for efficient image representation and image recognition. The limitation of the conventional sparse coding is that a multi-dimensional data (e.g. an image or a video image) should be unfolded into a vector resulting in loss of spatial and spatial-temporal relationship of the data. In this keynote talk, I will talk about anew tensor sparse coding method and its application to multi-dimensional medical image analysis, in which the multi-dimensional data can be treated as a tensor without unfolding.