Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection. (arXiv:2203.12121v2 [cs.CV] UPDATED)

Current polyp detection methods from colonoscopy videos use exclusively
normal (i.e., healthy) training images, which i) ignore the importance of
temporal information in consecutive video frames, and ii) lack knowledge about
the polyps. Consequently, they often have high detection errors, especially on
challenging polyp cases (e.g., small, flat, or partially visible polyps). In
this work, we formulate polyp detection as a weakly-supervised anomaly
detection task that uses video-level labelled training data to detect
frame-level polyps. In particular, we propose a …

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