Bio Imaging, Signal Processing & Learning (BISPL)
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    • NIRS-SPM
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    • Patch Low Rank MRI
    • Sparse SPM
    • ALOHA Inpainting
    • Robust ALOHA
    • ALOHA for MR Recon
    • MR Artifact Removal using Robust ALOHA
    • MR Ghost Artifact correction using ALOHA
    • Geometric GAN
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Latest Research Highlight
​Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems 

Ye et al, SIAM Journal on Imaging Sciences (in press), 2018

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IEEE EMBS International Summer School on Biomedical Imaging

6/14/2018

 
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 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:
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summer-school-part1_public.pdf
File Size: 30224 kb
File Type: pdf
Download File
summer-school-part2_public.pdf
File Size: 57059 kb
File Type: pdf
Download File
summer-school-part3_public.pdf
File Size: 67955 kb
File Type: pdf
Download File
Important Notice to Summer School Students: 
  • In order to complile the MatConvNet packages,  you need to install Visual Studio ++ (window user) or Xcode (Mac User). Otherwise, you may see the error message during the compilation.
  • If you have any questions in running the code, you can drop me an email to jong.ye@kaist.ac.kr
  • I am also looking for postdoc and Ph.D students. If you and/or your friends are interested in, please let me know.

2018 CT Meeting Refresher Course on Deep Learning for CT

5/23/2018

 
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The refresher course presentation material "Deep Learning for CT Reconstruction" can be downloaded from the following link.
ct_meeting_final_jcy.pdf
File Size: 42507 kb
File Type: pdf
Download File
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    BISPL@KAIST

    BISPL is a group of people at KAIST who are eager to dedicate their time and effort to investigate the beauty of bio- and medical imaging with the help of mathematics, machine learning, and physics.

    Our Location
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. 
CONTACT US
Bio Imaging. Signal Processing & Learning
Dept. of Bio & Brain Engineering 
KAIST
291 Daehak-ro, Yuseong-gu
Daejeon 305-701, Korea

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All Rights Reserved.
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