83 citations · 756 across the 59 of their papers we have counts for
22 papers · 1 filter
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation
Boah Kim, Yujin Oh, Bradford J. Wood +2
Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in i…
Generative AI for Medical Imaging: extending the MONAI Framework
Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot +21
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to he…
Annealed Score-Based Diffusion Model for MR Motion Artifact Reduction
Gyutaek Oh, Jeong Eun Lee, Jong Chul Ye
Motion artifact reduction is one of the important research topics in MR imaging, as the motion artifact degrades image quality and makes diagnosis difficult. Recently, many deep le…
Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation
Boah Kim, Yujin Oh, Jong Chul Ye
Vessel segmentation in medical images is one of the important tasks in the diagnosis of vascular diseases and therapy planning. Although learning-based segmentation approaches have…
Self-supervised Multi-modal Training from Uncurated Image and Reports Enables Zero-shot Oversight Artificial Intelligence in Radiology
Sangjoon Park, Eun Sun Lee, Kyung Sook Shin +2
Oversight AI is an emerging concept in radiology where the AI forms a symbiosis with radiologists by continuously supporting radiologists in their decision-making. Recent advances…
Patch-wise Deep Metric Learning for Unsupervised Low-Dose CT Denoising
Chanyong Jung, Joonhyung Lee, Sunkyoung You +1
The acquisition conditions for low-dose and high-dose CT images are usually different, so that the shifts in the CT numbers often occur. Accordingly, unsupervised deep learning-bas…
Diffusion Deformable Model for 4D Temporal Medical Image Generation
Boah Kim, Jong Chul Ye
Temporal volume images with 3D+t (4D) information are often used in medical imaging to statistically analyze temporal dynamics or capture disease progression. Although deep-learnin…
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion
Hyungjin Chung, Eun Sun Lee, Jong Chul Ye
Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of such images. As a method to mitigate such artifact, denoising is largely studied both wit…
AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation
Sangjoon Park, Gwanghyun Kim, Yujin Oh +7
Although deep learning-based computer-aided diagnosis systems have recently achieved expert-level performance, developing a robust deep learning model requires large, high-quality…
Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment
Yujin Oh, Go Eun Bae, Kyung-Hee Kim +2
Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. Although artificial i…
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…
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…
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…
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…
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…
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…
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…
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…