2 citations · 2 across the 11 of their papers we have counts for
13 papers
CHARTER: Auditing Reference Substitution in Hierarchical Compact-Evidence Evaluation for Computational Pathology
Hyun Do Jung, Jungwon Choi, Soojung Choi +2
In digital pathology, compact evidence is often used to explain or audit predictions made by whole-slide image multiple instance learning models. In hierarchical compact-evidence p…
Are Compact Rationales Free? Measuring Tile Selection Headroom in Frozen WSI-MIL
Hyun Do Jung, Jungwon Choi, Soojung Choi +2
Whole-slide image (WSI) multiple instance learning (MIL) classifiers can achieve strong slide-level AUC while leaving the full-bag prediction opaque. Attention scores are widely re…
DuetMoE: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Analysis
Yiqi Tian, Sangjoon Park, Jinwoong Park +4
As medical AI expands across diverse healthcare settings worldwide, equitable performance across patient populations is becoming essential to trustworthy clinical use. Fairness in…
CoMMa: Contribution-Aware Medical Multi-Agents for Decentralized Oncology Decision Support
Yichen Wu, Kailong Fan, Sangjoon Park +9
Recent multi-agent frameworks have shown promise for oncology decision support, yet most assume centralized data access and rely on prompt-based assignment, limiting their applicab…
Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface
Siyeop Yoon, Yujin Oh, Pengfei Jin +5
We present Surf2CT, a novel cascaded flow matching framework that synthesizes full 3D computed tomography (CT) volumes of the human torso from external surface scans and simple dem…
Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographics
Siyeop Yoon, Sifan Song, Pengfei Jin +6
We propose a cascaded 3D diffusion model framework to synthesize high-fidelity 3D PET/CT volumes directly from demographic variables, addressing the growing need for realistic digi…
OWT: A Foundational Organ-Wise Tokenization Framework for Medical Imaging
Sifan Song, Siyeop Yoon, Pengfei Jin +10
Recent advances in representation learning often rely on holistic embeddings that entangle multiple semantic components, limiting interpretability and generalization. These issues…
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts
Runqi Meng, Sifan Song, Pengfei Jin +9
Accurate tumor segmentation is crucial for cancer diagnosis and treatment. While foundation models have advanced general-purpose segmentation, existing methods still struggle with:…
Prediction of Frozen Region Growth in Kidney Cryoablation Intervention Using a 3D Flow-Matching Model
Siyeop Yoon, Yujin Oh, Matthew Tivnan +7
This study presents a 3D flow-matching model designed to predict the progression of the frozen region (iceball) during kidney cryoablation. Precise intraoperative guidance is criti…
Developing a PET/CT Foundation Model for Cross-Modal Anatomical and Functional Imaging
Yujin Oh, Robert Seifert, Yihan Cao +15
In oncology, Positron Emission Tomography-Computed Tomography (PET/CT) is widely used in cancer diagnosis, staging, and treatment monitoring, as it combines anatomical details from…
Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective
Yujin Oh, Pengfei Jin, Sangjoon Park +6
Ensuring fairness in medical image segmentation is critical due to biases in imbalanced clinical data acquisition caused by demographic attributes (e.g., age, sex, race) and clinic…
Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation
Yujin Oh, Sangjoon Park, Xiang Li +21
Clinical decision-making reflects diverse strategies shaped by regional patient populations and institutional protocols. However, most existing medical artificial intelligence (AI)…
High-Fidelity 3D Lung CT Synthesis in ARDS Swine Models Using Score-Based 3D Residual Diffusion Models
Siyeop Yoon, Yujin Oh, Xiang Li +3
Acute respiratory distress syndrome (ARDS) is a severe condition characterized by lung inflammation and respiratory failure, with a high mortality rate of approximately 40%. Tradit…