83 citations · 756 across the 59 of their papers we have counts for
21 papers · 1 filter
Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching
Kwanyoung Kim, Taesung Kwon, Jong Chul Ye
Tweedie distributions are a special case of exponential dispersion models, which are often used in classical statistics as distributions for generalized linear models. Here, we rev…
DiffuseMorph: Unsupervised Deformable Image Registration Using Diffusion Model
Boah Kim, Inhwa Han, Jong Chul Ye
Deformable image registration is one of the fundamental tasks in medical imaging. Classical registration algorithms usually require a high computational cost for iterative optimiza…
Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction
Hyungjin Chung, Byeongsu Sim, Jong Chul Ye
Diffusion models have recently attained significant interest within the community owing to their strong performance as generative models. Furthermore, its application to inverse pr…
CLIPstyler: Image Style Transfer with a Single Text Condition
Gihyun Kwon, Jong Chul Ye
Existing neural style transfer methods require reference style images to transfer texture information of style images to content images. However, in many practical situations, user…
Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training
Sangjoon Park, Gwanghyun Kim, Jeongsol Kim +2
Federated learning, which shares the weights of the neural network across clients, is gaining attention in the healthcare sector as it enables training on a large corpus of decentr…
Performance Analysis of Fractional Learning Algorithms
Abdul Wahab, Shujaat Khan, Imran Naseem +1
Fractional learning algorithms are trending in signal processing and adaptive filtering recently. However, it is unclear whether the proclaimed superiority over conventional algori…
Score-based diffusion models for accelerated MRI
Hyungjin Chung, Jong Chul Ye
Score-based diffusion models provide a powerful way to model images using the gradient of the data distribution. Leveraging the learned score function as a prior, here we introduce…
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation
Gwanghyun Kim, Taesung Kwon, Jong Chul Ye
Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining (CLIP) enables zero-shot image manipulation guided by text prompts. However, their applications…
Deep Learning for Ultrasound Beamforming
Ruud JG van Sloun, Jong Chul Ye, Yonina C Eldar
Diagnostic imaging plays a critical role in healthcare, serving as a fundamental asset for timely diagnosis, disease staging and management as well as for treatment choice, plannin…
Federated CycleGAN for Privacy-Preserving Image-to-Image Translation
Joonyoung Song, Jong Chul Ye
Unsupervised image-to-image translation methods such as CycleGAN learn to convert images from one domain to another using unpaired training data sets from different domains. Unfort…
Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images
Kwanyoung Kim, Jong Chul Ye
Recently, there has been extensive research interest in training deep networks to denoise images without clean reference. However, the representative approaches such as Noise2Noise…
Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention
Byung-Hoon Kim, Jong Chul Ye, Jae-Jin Kim
Functional connectivity (FC) between regions of the brain can be assessed by the degree of temporal correlation measured with functional neuroimaging modalities. Based on the fact…
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement
Mehmet Akçakaya, Burhaneddin Yaman, Hyungjin Chung +1
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with…
Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning
Hyungjin Chung, Jaehyun Kim, Jeong Hee Yoon +2
Diffusion-weighted MRI is nowadays performed routinely due to its prognostic ability, yet the quality of the scans are often unsatisfactory which can subsequently hamper the clinic…
Feature Disentanglement in generating three-dimensional structure from two-dimensional slice with sliceGAN
Hyungjin Chung, Jong Chul Ye
Deep generative models are known to be able to model arbitrary probability distributions. Among these, a recent deep generative model, dubbed sliceGAN, proposed a new way of using…
Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoising
Taesung Kwon, Jong Chul Ye
Recently, CycleGAN was shown to provide high-performance, ultra-fast denoising for low-dose X-ray computed tomography (CT) without the need for a paired training dataset. Although…
Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification
Sangjoon Park, Gwanghyun Kim, Yujin Oh +6
Developing a robust algorithm to diagnose and quantify the severity of COVID-19 using Chest X-ray (CXR) requires a large number of well-curated COVID-19 datasets, which is difficul…
Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy
Hyoungjun Park, Myeongsu Na, Bumju Kim +4
Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution from inferior axial resolution compared to the lateral resolution. To address this…
CXR Segmentation by AdaIN-based Domain Adaptation and Knowledge Distillation
Yujin Oh, Jong Chul Ye
As segmentation labels are scarce, extensive researches have been conducted to train segmentation networks with domain adaptation, semi-supervised or self-supervised learning techn…
Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and Translation
Gihyun Kwon, Jong Chul Ye
One of the important research topics in image generative models is to disentangle the spatial contents and styles for their separate control. Although StyleGAN can generate content…
Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain
Hyungjin Chung, Jaeyoung Huh, Geon Kim +2
Optical diffraction tomography (ODT) produces three dimensional distribution of refractive index (RI) by measuring scattering fields at various angles. Although the distribution of…