Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

@article{Tian2022ContrastiveTM,
  title={Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection},
  author={Yu Tian and Guansong Pang and Fengbei Liu and Yuyuan Liu and Chongjian Wang and Yuanhong Chen and Johan W. Verjans and G. Carneiro},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.12121}
}
. 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… 

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