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
Colorectal cancer remains a leading cause of cancer-related mortality worldwide, emphasizing the importance of early detection through accurate polyp identification. However, colonoscopy relies heavily on precise polyp segmentation in endoscopic images, yet this task remains challenging due to morphological variability, low contrast, and imaging artifacts. In this study, we propose HSSAM-Net, a lightweight deep learning framework that integrates a Hyper-Scale Shifted Aggregation Module to capture multi-scale contextual information while preserving fine-grained details, Progressive Reuse Attention mechanism that strengthens feature propagation across the encoder-decoder pathway, and Max-Diagonal Pooling/Unpooling (MaxDP/MaxDUP) a novel dual-branch sampling scheme to improve texture representation, feature alignment to enhance feature aggregation, context learning, and boundary refinement. The proposed model is evaluated on five benchmark datasets (Kvasir, CVC-ClinicDB, ETIS, CVC-300, EndoCV2020). Experimental results show that HSSAM-Net consistently outperforms state-of-the-art methods across benchmark datasets, HSSAM-Net consistently achieves state-of-the-art accuracy (Dice: 0.949-0.952, mIoU: 0.924-0.930), while maintaining real-time efficiency at 24.1 FPS with only 0.9 M parameters. Furthermore, an analysis of trainable parameters and inference speed confirms its suitability for real-time clinical applications. Our findings demonstrate that HSSAM-Net achieves a favorable trade-off between accuracy and efficiency, advancing the development of practical and reliable computer-aided colonoscopy systems.
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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