인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Medical image segmentation is vital for precise identification and analysis of anatomical structures and pathological regions, yet traditional models often fall short in aligning with clinical workflows, requiring extensive manual correction even when overall segmentation accuracy is high. To address this gap, we introduce HybridMS, a hybrid intelligence framework designed to maintain high segmentation accuracy while substantially reducing clinician workload through selective human intervention. HybridMS employs an uncertainty-driven feedback mechanism that selectively triggers clinician input only for cases predicted to be challenging, thereby avoiding unnecessary manual review. Corrected cases are prioritised during retraining through a weighted update strategy, enabling the model to adapt more effectively to clinically relevant errors. This design minimises intervention frequency while preserving segmentation quality. Evaluated on lung segmentation in chest X-rays for tuberculosis detection, HybridMS achieved comparable or improved performance over the baseline MedSAM model (Dice: 0.9538 vs. 0.9435; IoU: 0.9126 vs. 0.8941) with consistent boundary quality in difficult cases. For the subset of cases identified as challenging (baseline Dice < 0.92), HybridMS reduced mean Hausdorff Distance and Average Symmetric Surface Distance, demonstrating more stable anatomical boundaries. Workflow efficiency was markedly improved: in a preliminary timing study with radiologists, average annotation time was reduced by approximately 82% for standard cases and 60% for challenging cases, without compromising accuracy. By combining targeted human oversight with automated refinement, HybridMS demonstrates that stable segmentation performance can be achieved with significantly lower annotation effort, offering a clinically viable pathway for efficient and reliable deployment in diagnostic workflows.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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