1 citations · 1 across the 11 of their papers we have counts for
14 papers
XFlow: A Workflow Model for Instruction-Guided Lesion Segmentation in Chest X-rays
Geon Choi, Hangyul Yoon, Hyunki Park +2
Existing text-guided segmentation models in the medical domain cover only a narrow set of anatomical structures and lesions in chest X-rays (CXRs), and most of them assume that the…
SentZero: An Enhanced Sentence-Centric Vision-Language Pretraining for Multi-Task Zero-Shot Chest X-Ray Analysis
Hangyul Yoon, Hyungyung Lee, Edward Choi +1
Vision-language (VL) pretraining using paired chest X-ray (CXR) images and radiology reports has shown strong potential for medical image understanding. However, existing methods o…
CheXpercept: A Benchmark for Evaluating Expert-Level Lesion Perception in Chest X-rays
Geon Choi, Hangyul Yoon, Nalee Kim +5
The evaluation of vision-language models (VLMs) for chest X-ray (CXR) analysis has largely been limited to disease-presence classification without visual grounding. Such evaluation…
EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering over Longitudinal Discharge Summaries
Jiyoun Kim, Muhan Yeo, Eunhye Jang +14
Discharge summaries are crucial clinical documents containing the context of a patient's overall hospital stay, and are routinely reviewed by medical experts for patient readmissio…
TRIAGE: Dialectical LLM Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series
Hyeongwon Jang, Gyouk Chu, Changhun Kim +4
Clinical early warning systems built on irregularly sampled medical time series (ISMTS) from electronic health records must deliver continuous risk scores for patient triage as wel…
CXReasonAgent: Evidence-Grounded Diagnostic Reasoning Agent for Chest X-rays
Hyungyung Lee, Hangyul Yoon, Edward Choi
Chest X-ray plays a central role in thoracic diagnosis, and its interpretation inherently requires multi-step, evidence-grounded reasoning. However, large vision-language models (L…
ECG-Reasoning-Benchmark: A Benchmark for Evaluating Clinical Reasoning Capabilities in ECG Interpretation
Jungwoo Oh, Hyunseung Chung, Junhee Lee +6
While Multimodal Large Language Models (MLLMs) show promising performance in automated electrocardiogram interpretation, it remains unclear whether they genuinely perform actual st…
Instruction-Guided Lesion Segmentation for Chest X-rays with Automatically Generated Large-Scale Dataset
Geon Choi, Hangyul Yoon, Hyunju Shin +4
The applicability of current lesion segmentation models for chest X-rays (CXRs) has been limited both by a small number of target labels and the reliance on complex, expert-level t…
Modeling Clinical Uncertainty in Radiology Reports: from Explicit Uncertainty Markers to Implicit Reasoning Pathways
Paloma Rabaey, Jong Hak Moon, Jung-Oh Lee +4
Radiology reports are invaluable for clinical decision-making and hold great potential for automated analysis when structured into machine-readable formats. These reports often con…
Lunguage: A Benchmark for Structured and Sequential Chest X-ray Interpretation
Jong Hak Moon, Geon Choi, Paloma Rabaey +10
Radiology reports convey detailed clinical observations and capture diagnostic reasoning that evolves over time. However, existing evaluation methods are limited to single-report s…
CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays
Hyungyung Lee, Geon Choi, Jung-Oh Lee +3
Recent progress in Large Vision-Language Models (LVLMs) has enabled promising applications in medical tasks, such as report generation and visual question answering. However, exist…
Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain
Hangyul Yoon, Doohyuk Jang, Jungeun Kim +1
Leveraging pre-trained models with tailored prompts for in-context learning has proven highly effective in NLP tasks. Building on this success, recent studies have applied a simila…
Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images
JungEun Kim, Hangyul Yoon, Geondo Park +2
4D medical images, which represent 3D images with temporal information, are crucial in clinical practice for capturing dynamic changes and monitoring long-term disease progression.…
EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries
Sunjun Kweon, Jiyoun Kim, Heeyoung Kwak +6
Discharge summaries in Electronic Health Records (EHRs) are crucial for clinical decision-making, but their length and complexity make information extraction challenging, especiall…