Helen Hong
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Research Interests
Helen Hong has a total of 67 co-authors. Here are the top co-authors by number of shared publications:
- Min Jin Lee
Seoul Women’s University
9 shared publications - Kyu-Won Shim
Yonsei University College of Medicine
6 shared publications Hansang Lee
Department of Software Convergence Seoul Women's University Seoul Korea
5 shared publications- Hansang Lee
Seoul Women’s University, Nowon-gu
5 shared publications - Joon Seok Lim
Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
5 shared publications - Jinseo An
Seoul Women’s University
4 shared publications
Latest Publications
- Open Access
- 1 September 2026
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 32 (4) , 91-100
Universal Segmentation of Kidney, Tumor, and Cyst Using Support Image-Mask Pair-Based Visual Conditioning
사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.
- 1 September 2026
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 32 (4) , 91-100
Open AccessUniversal Segmentation of Kidney, Tumor, and Cyst Using Support Image-Mask Pair-Based Visual Conditioning
사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.
- Open Access
- 1 September 2026
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 32 (4) , 57-66
Prostate Cancer Aggressiveness Prediction Using Class-conditional Test-Time Augmentation
사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.
- 1 September 2026
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 32 (4) , 57-66
Open AccessProstate Cancer Aggressiveness Prediction Using Class-conditional Test-Time Augmentation
사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.
- Open Access
- 1 September 2026
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 32 (4) , 47-56
LoRA-Based Multimodal Foundation Model Adaptation for MR Meniscus Segmentation
사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.
- 1 September 2026
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 32 (4) , 47-56
Open AccessLoRA-Based Multimodal Foundation Model Adaptation for MR Meniscus Segmentation
사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.
- Open Access
- 1 June 2026
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 32 (2) , 23-31
Attention-based Multiple Instance Learning for Classification of Inflammatory Activity in Crohn’s Disease Patients from MR Enterography
사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.
- 1 June 2026
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 32 (2) , 23-31
Open AccessAttention-based Multiple Instance Learning for Classification of Inflammatory Activity in Crohn’s Disease Patients from MR Enterography
사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.
- Open Access
- 1 March 2026
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 32 (1) , 13-20
Joint Image-Mask Conditional Diffusion for Robust Meniscus Segmentation in Knee MR Images
- 1 March 2026
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 32 (1) , 13-20
Open AccessJoint Image-Mask Conditional Diffusion for Robust Meniscus Segmentation in Knee MR Images
- Open Access
DeFloMat: Detection with Flow Matching for Stable and Efficient Generative Object Localization
We propose DeFloMat (Detection with Flow Matching), a novel generative object detection framework that addresses the critical latency bottleneck of diffusion-based detectors, such as DiffusionDet, by integrating Conditional Flow Matching (CFM). Diffusion models achieve high accuracy by formulating detection as a multi-step stochastic denoising process, but their reliance on numerous sampling steps ($T \gg 60$) makes them impractical for time-sensitive clinical applications like Crohn's Disease detection in Magnetic Resonance Enterography (MRE). DeFloMat replaces this slow stochastic path with a highly direct, deterministic flow field derived from Conditional Optimal Transport (OT) theory, specifically approximating the Rectified Flow. This shift enables fast inference via a simple Ordinary Differential Equation (ODE) solver. We demonstrate the superiority of DeFloMat on a challenging MRE clinical dataset. Crucially, DeFloMat achieves state-of-the-art accuracy ($43.32\% \text{ } AP_{10:50}$) in only $3$ inference steps, which represents a $1.4\times$ performance improvement over DiffusionDet's maximum converged performance ($31.03\% \text{ } AP_{10:50}$ at $4$ steps). Furthermore, our deterministic flow significantly enhances localization characteristics, yielding superior Recall and stability in the few-step regime. DeFloMat resolves the trade-off between generative accuracy and clinical efficiency, setting a new standard for stable and rapid object localization.Open AccessDeFloMat: Detection with Flow Matching for Stable and Efficient Generative Object Localization
We propose DeFloMat (Detection with Flow Matching), a novel generative object detection framework that addresses the critical latency bottleneck of diffusion-based detectors, such as DiffusionDet, by integrating Conditional Flow Matching (CFM). Diffusion models achieve high accuracy by formulating detection as a multi-step stochastic denoising process, but their reliance on numerous sampling steps ($T \gg 60$) makes them impractical for time-sensitive clinical applications like Crohn's Disease detection in Magnetic Resonance Enterography (MRE). DeFloMat replaces this slow stochastic path with a highly direct, deterministic flow field derived from Conditional Optimal Transport (OT) theory, specifically approximating the Rectified Flow. This shift enables fast inference via a simple Ordinary Differential Equation (ODE) solver. We demonstrate the superiority of DeFloMat on a challenging MRE clinical dataset. Crucially, DeFloMat achieves state-of-the-art accuracy ($43.32\% \text{ } AP_{10:50}$) in only $3$ inference steps, which represents a $1.4\times$ performance improvement over DiffusionDet's maximum converged performance ($31.03\% \text{ } AP_{10:50}$ at $4$ steps). Furthermore, our deterministic flow significantly enhances localization characteristics, yielding superior Recall and stability in the few-step regime. DeFloMat resolves the trade-off between generative accuracy and clinical efficiency, setting a new standard for stable and rapid object localization. - Open Access
- 1 December 2025
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 31 (5) , 1-7
Predicting Prostate Cancer Aggressiveness via Diffusion Autoencoders with Multi-Parametric MR Images
- 1 December 2025
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 31 (5) , 1-7
Open AccessPredicting Prostate Cancer Aggressiveness via Diffusion Autoencoders with Multi-Parametric MR Images
- 20 September 2025
- Published by Springer Nature
- p. 213-221
From Variability To Accuracy: Conditional Bernoulli Diffusion Models with Consensus-Driven Correction for Thin Structure Segmentation
- 20 September 2025
- Published by Springer Nature
- p. 213-221
From Variability To Accuracy: Conditional Bernoulli Diffusion Models with Consensus-Driven Correction for Thin Structure Segmentation
- Open Access
From Variability To Accuracy: Conditional Bernoulli Diffusion Models with Consensus-Driven Correction for Thin Structure Segmentation
Accurate segmentation of orbital bones in facial computed tomography (CT) images is essential for the creation of customized implants for reconstruction of defected orbital bones, particularly challenging due to the ambiguous boundaries and thin structures such as the orbital medial wall and orbital floor. In these ambiguous regions, existing segmentation approaches often output disconnected or under-segmented results. We propose a novel framework that corrects segmentation results by leveraging consensus from multiple diffusion model outputs. Our approach employs a conditional Bernoulli diffusion model trained on diverse annotation patterns per image to generate multiple plausible segmentations, followed by a consensus-driven correction that incorporates position proximity, consensus level, and gradient direction similarity to correct challenging regions. Experimental results demonstrate that our method outperforms existing methods, significantly improving recall in ambiguous regions while preserving the continuity of thin structures. Furthermore, our method automates the manual process of segmentation result correction and can be applied to image-guided surgical planning and surgery.
Open AccessFrom Variability To Accuracy: Conditional Bernoulli Diffusion Models with Consensus-Driven Correction for Thin Structure Segmentation
Accurate segmentation of orbital bones in facial computed tomography (CT) images is essential for the creation of customized implants for reconstruction of defected orbital bones, particularly challenging due to the ambiguous boundaries and thin structures such as the orbital medial wall and orbital floor. In these ambiguous regions, existing segmentation approaches often output disconnected or under-segmented results. We propose a novel framework that corrects segmentation results by leveraging consensus from multiple diffusion model outputs. Our approach employs a conditional Bernoulli diffusion model trained on diverse annotation patterns per image to generate multiple plausible segmentations, followed by a consensus-driven correction that incorporates position proximity, consensus level, and gradient direction similarity to correct challenging regions. Experimental results demonstrate that our method outperforms existing methods, significantly improving recall in ambiguous regions while preserving the continuity of thin structures. Furthermore, our method automates the manual process of segmentation result correction and can be applied to image-guided surgical planning and surgery.
- Open Access
- 1 June 2025
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 31 (2) , 27-36
Semi-supervised meniscus segmentation with adaptive weighting strategies in a limited labeled data environment
사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.
- 1 June 2025
- Published by Korea Computer Graphics Society in Journal of the Korea Computer Graphics Society
- Vol. 31 (2) , 27-36
Open AccessSemi-supervised meniscus segmentation with adaptive weighting strategies in a limited labeled data environment
사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.