WSPolypNet: Weakly Supervised Polyp Localization in Colonoscopy Videos
Quick summary
arXiv:2609.08182v1 Announce Type: cross 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 se
Key takeaways
- arXiv:2609.08182v1 Announce Type: cross 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.
Why it matters
“WSPolypNet: Weakly Supervised Polyp Localization in Colonoscopy Videos” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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