BISPL- BioImaging, Signal Processing & Learning Lab @ KAIST AI
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Latest Research Highlight
Improving Diffusion Models for Inverse Problems using Manifold Constraints

Chung, H., et al. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022. 

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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.

Two papers accepted to ECCV 2022

7/6/2022

 
Two papers are accepted to ECCV 2022!
  1. Yujin Oh, and Jong Chul Ye, "CXR Segmentation by AdaIN-based Domain Adaptation and Knowledge Distillation", European Conference on Computer Vision (ECCV), 2022.
  2. Boah Kim, Inhwa Han, and Jong Chul Ye, "DiffuseMorph: Unsupervised Deformable Image Registration  Using Diffusion Models",European Conference on Computer Vision (ECCV), 2022

​​​This year we have  2 ECCV papers, 5 CVPR papers,  2 MICCAI papers, 3 Nature papers (2 Nature Comm, 1 Nature Review Cardiology), 1 ACS Nano,  2 MEDIA,  2 IEEE Journals (TMI, SPM). 

Our Latest Deep Learning Paper published in ACS Nano  (IF=15.88)

6/27/2022

 
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Five papers accepted to CVPR 2022!

3/2/2022

 
Congraturations!  Five papers from BISPL are finally accepted for CVPR 2022. The list of the accepted papers is as follows.
  1. Gwanghyun Kim, and Jong Chul Ye. "DiffusionCLIP: Text-guided image manipulation using diffusion models," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  2. Chanyong Jung, Gihyun Kwon, Jong Chul Ye, "Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks",IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  3. Gihyun Kwon, and Jong Chul Ye. "CLIPstyler: Image style transfer with a single text condition,"IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  4. Kwanyoung Kim, Taesung Kwon, and Jong Chul Ye. "Noise distribution adaptive self-supervised image denoising using Tweedie distribution and score matching", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  5. Hyungjin Chung, Byeongsu Sim, and Jong Chul Ye. "Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction",IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
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BISPL's new home @ Graduate School of AI

2/9/2022

 
BISPL has joined the Graduate School of AI as of January 3rd, 2022 in order to focus more on the machine learning and artificial intelligence for healthcare.  Accordingly, we are accepting students only through the Graduate School of AI.  For students who are interested in joining BISPL,  please apply to the Graduate School of AI (https://gsai.kaist.ac.kr/admission/​) or contact Prof. Jong Chul Ye.
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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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