WSPolypNet: Weakly Supervised Polyp Localization in Colonoscopy Videos

2026-09-08

AI Summary

为解决结肠镜视频中息肉定位的密集标注成本问题，提出WSPolypNet框架，仅使用视频级标签通过3D卷积神经网络生成类激活图，并结合多视角策略和MedSAM2细化定位，有效减少空间标注需求。

Abstract

Because dense frame-level annotation of colonoscopy videos is costly, we propose WSPolypNet, a weakly supervised framework for polyp localization using only video-level labels. WSPolypNet employs a 3D convolutional neural network trained with video-level supervision to generate class activation maps (CAMs), which identify candidate polyp regions without requiring frame-level spatial annotations. The CAM-derived localization cues are further enhanced using a multi-view strategy and provided to MedSAM2 as point prompts. MedSAM2 then propagates segmentation masks across the video, refining the coarse localization cues according to polyp boundaries. WSPolypNet achieved CorLoc scores of 47.80%, 43.68%, and 35.01% at IoU thresholds of 0.3, 0.5, and 0.7, respectively, compared with 36.87%, 33.72%, and 27.94% in the single-view setting. For small polyps, the multi-view strategy improved CorLoc@0.5 from 16.01% to 30.97%. The framework also achieved a recall of 94.51%. These results demonstrate the potential of weakly supervised spatiotemporal learning to substantially reduce spatial annotation requirements for polyp localization in colonoscopy videos.

Problem

• weakly supervised
• polyp localization
• colonoscopy videos
• video-level labels
• spatiotemporal learning

Innovation

• Weakly Supervised Learning
• Polyp Localization
• Class Activation Maps (CAMs)
• Multi-view Strategy
• Colonoscopy Videos