156 citations · 237 across the 7 of their papers we have counts for
8 papers
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…
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…
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…
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…
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…
CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration
Boah Kim, Dong Hwan Kim, Seong Ho Park +3
Image registration is a fundamental task in medical image analysis. Recently, deep learning based image registration methods have been extensively investigated due to their excelle…
Unsupervised Deformable Image Registration Using Cycle-Consistent CNN
Boah Kim, Jieun Kim, June-Goo Lee +3
Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis. Recently, deep learning based supervised and unsupervised imag…
Mumford-Shah Loss Functional for Image Segmentation with Deep Learning
Boah Kim, Jong Chul Ye
Recent state-of-the-art image segmentation algorithms are mostly based on deep neural networks, thanks to their high performance and fast computation time. However, these methods a…