BISPL- BioImaging, Signal Processing & Learning Lab @ KAIST AI
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    • NIRS-SPM
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    • ALOHA Inpainting
    • ALOHA for MR Recon
    • MR Artifact Removal using Robust ALOHA
    • MR Ghost Artifact correction using ALOHA
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Latest Research Highlight
Diffusion-based Image Translation using Disentangled Style and Content Representation

Kwon, G., et al. International Conference on Learning Representation (ICLR),  2023. 

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Three papers accepted to ICLR 2023!

1/21/2023

 
Congraturation! The following three papers from BISPL are accepted to ICLR 2023. One of them is for spot-light presentation.
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  1. Hyungjin Chung, Jeongsol Kim, Michael Thompson Mccann, Marc Louis Klasky, Jong Chul Ye, "Diffusion Posterior Sampling for General Noisy Inverse Problems", International Conference on Learning Representations (ICLR), 2023  Spotlight (notable-top-25%) Presentation.
  2. Boah Kim, Yujin Oh, Jong Chul Ye,"Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation", International Conference on Learning Representations (ICLR), 2023 
  3. Gihyun Kwon, Jong Chul Ye, "Diffusion-based Image Translation using disentangled style and content representation",  International Conference on Learning Representations (ICLR), 2023 

Recent Paper in Nature Machine Intelligence

11/4/2022

 
Lee et al, "Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data",  Nature Machine Intelligence (in press), 2022.

​This year we have  4 Nature Papers (2 Nature Comm,  1 Nature Machine Intelligence, 1 Nature Review Cardiology).
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Two papers accepted to NeurIPS 2022

9/14/2022

 
Congratulations! The following two papers from BISPL are accepted to NeurIPS 2022!
  • Beomsu Kim, Jong Chul Ye, “Energy-Based Contrastive Learning of Visual Representations”, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS) 2022.
  • Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, Jong Chul Ye, “Improving Diffusion Models for Inverse Problems using Manifold Constraints”,Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS) 2022.
​This year so far we have  2 NeurIPS papers,  2 ECCV papers, 5 CVPR papers,  2 MICCAI papers, 3 Nature, 1 ACS Nano,  2 MEDIA,  2 IEEE (TMI, SPM), 1 JACC papers.
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    BISPL@KAIST AI

    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.
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
Graduate School of AI
KAIST
291 Daehak-ro, Yuseong-gu
Daejeon 305-701, Korea

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