6 papers
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…