YOLO-LAN提升肠镜下息肉检测精度，关键在损失函数与数据增强优化。
YOLO-LAN: Precise Polyp Detection via Optimized Loss, Augmentations and Negatives
- 采用M2IoU损失+多样化增强和负样本，模拟真实临床场景训练
- 在Kvasir-seg数据集上达mAP₅₀: 0.9619，mAP₅₀:₉₅: 0.8599
- 对小尺寸息肉定位精准，适合临床AI辅助筛查应用
结直肠癌始于结肠内壁异常黏膜细胞增生形成的息肉，若未被发现可能演变为恶性肿瘤。肠镜检查是标准检测手段，可直接观察并切除可疑病灶，但人工判读易出现遗漏或不一致。基于深度学习的目标检测能实现更精准、实时的诊断。本文提出YOLO-LAN，一种基于YOLO的息肉检测框架，使用M2IoU损失函数、多样数据增强及负样本训练，以模拟真实临床环境。该方法在Kvasir-seg和BKAI-IGH NeoPolyp数据集上优于现有模型，于Kvasir-seg数据集上取得mAP₅₀ 0.9619、mAP₅₀:₉₅ 0.8599（YOLOv12），以及mAP₅₀ 0.9540、mAP₅₀:₉₅ 0.8487（YOLOv8）的成绩。显著提升的mAP₅₀:₉₅表明检测精度增强，且对不同大小息肉具有鲁棒性，具备临床实用价值。
原文摘要 · Abstract (English)
Colorectal cancer (CRC), a lethal disease, begins with the growth of abnormal mucosal cell proliferation called polyps in the inner wall of the colon. When left undetected, polyps can become malignant tumors. Colonoscopy is the standard procedure for detecting polyps, as it enables direct visualization and removal of suspicious lesions. Manual detection by colonoscopy can be inconsistent and is subject to oversight. Therefore, object detection based on deep learning offers a better solution for a more accurate and real-time diagnosis during colonoscopy. In this work, we propose YOLO-LAN, a YOLO-based polyp detection pipeline, trained using M2IoU loss, versatile data augmentations and negative data to replicate real clinical situations. Our pipeline outperformed existing methods for the Kvasir-seg and BKAI-IGH NeoPolyp datasets, achieving mAP$_{50}$ of 0.9619, mAP$_{50:95}$ of 0.8599 with YOLOv12 and mAP$_{50}$ of 0.9540, mAP$_{50:95}$ of 0.8487 with YOLOv8 on the Kvasir-seg dataset. The significant increase is achieved in mAP$_{50:95}$ score, showing the precision of polyp detection. We show robustness based on polyp size and precise location detection, making it clinically relevant in AI-assisted colorectal screening.
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