2 citations · 5 across the 9 of their papers we have counts for
9 papers
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement
Jihoon Cho, Seunghyuck Park, Jinah Park
Despite significant advancements in automatic brain tumor segmentation methods, their performance is not guaranteed when certain MR sequences are missing. Addressing this issue, it…
Volumetric Conditioning Module to Control Pretrained Diffusion Models for 3D Medical Images
Suhyun Ahn, Wonjung Park, Jihoon Cho +2
Spatial control methods using additional modules on pretrained diffusion models have gained attention for enabling conditional generation in natural images. These methods guide the…
Label-Efficient 3D Brain Segmentation via Complementary 2D Diffusion Models with Orthogonal Views
Jihoon Cho, Suhyun Ahn, Beomju Kim +8
Deep learning-based segmentation techniques have shown remarkable performance in brain segmentation, yet their success hinges on the availability of extensive labeled training data…
Principled Feature Disentanglement for High-Fidelity Unified Brain MRI Synthesis
Jihoon Cho, Jonghye Woo, Jinah Park
Multisequence Magnetic Resonance Imaging (MRI) provides a more reliable diagnosis in clinical applications through complementary information across sequences. However, in practice,…
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser
Jihoon Cho, Xiaofeng Liu, Fangxu Xing +4
Multimodal Magnetic Resonance (MR) Imaging plays a crucial role in disease diagnosis due to its ability to provide complementary information by analyzing a relationship between mul…
Hybrid-Fusion Transformer for Multisequence MRI
Jihoon Cho, Jinah Park
Medical segmentation has grown exponentially through the advent of a fully convolutional network (FCN), and we have now reached a turning point through the success of Transformer.…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
SOS: Score-based Oversampling for Tabular Data
Jayoung Kim, Chaejeong Lee, Yehjin Shin +4
Score-based generative models (SGMs) are a recent breakthrough in generating fake images. SGMs are known to surpass other generative models, e.g., generative adversarial networks (…
Domain-Robust Mitotic Figure Detection with Style Transfer
Youjin Chung, Jihoon Cho, Jinah Park
We propose a new training scheme for domain generalization in mitotic figure detection. Mitotic figures show different characteristics for each scanner. We consider each scanner as…