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Our deep learning paper on elastic source imaging is finally accepted by SIAM Journal on Applied Mathematics. Congratulations !
  The lecture material for "Sparse and deep learning approaches for biomedical image reconstruction" by Prof. Jong Chul Ye at 13th IEEE EMBS International Summer School on Biomedical Imaging can be downloaded from the following links: 
Important Notice to Summer School Students:
 The refresher course presentation material "Deep Learning for CT Reconstruction" can be downloaded from the following link.
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ABOUT US
Our research activities are primarily focused on the signal processing and machine learning for high-resolution high-sensitivity image reconstruction from real world bio-medical imaging systems. Such problems pose interesting challenges that often lead to investigations of fundamental problems in various branches of physics, mathematics, signal processing, biology, and medicine. While most of the biomedical imaging researchers are interested in addressing this problem using off-the-self tools from signal processing, machine learning, statistics, and optimization and combining their domain-specific knowledge, our approaches are unique in the sense that I believe that actual bio-medical imaging applications are a source of endless inspiration for new mathematical theories and we are eager to solve both specific applications and application-inspired fundamental theoretical problems.
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CONTACT US
Bio Imaging. Signal Processing & Learning
Dept. of Bio & Brain Engineering KAIST 291 Daehak-ro, Yuseong-gu Daejeon 305-701, Korea Copyright (c) 2014, BISPL All Rights Reserved. |
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