4 papers
MAESIL: Masked Autoencoder for Enhanced Self-supervised Medical Image Learning
Kyeonghun Kim, Hyeonseok Jung, Youngung Han +14
Training deep learning models for three-dimensional (3D) medical imaging, such as Computed Tomography (CT), is fundamentally challenged by the scarcity of labeled data. While pre-t…
CIPHER: Counterfeit Image Pattern High-level Examination via Representation
Kyeonghun Kim, Youngung Han, Seoyoung Ju +9
The rapid progress of generative adversarial networks (GANs) and diffusion models has enabled the creation of synthetic faces that are increasingly difficult to distinguish from re…
FOSCU: Feasibility of Synthetic MRI Generation via Duo-Diffusion Models for Enhancement of 3D U-Nets in Hepatic Segmentation
Youngung Han, Kyeonghun Kim, Seoyoung Ju +8
Medical image segmentation faces fundamental challenges including restricted access, costly annotation, and data shortage to clinical datasets through Picture Archiving and Communi…
3D-LLDM: Label-Guided 3D Latent Diffusion Model for Improving High-Resolution Synthetic MR Imaging in Hepatic Structure Segmentation
Kyeonghun Kim, Jaehyeok Bae, Youngung Han +10
Deep learning and generative models are advancing rapidly, with synthetic data increasingly being integrated into training pipelines for downstream analysis tasks. However, in medi…