A noise-aware contrastive loss derived from temporal structure in colonoscopy videos produces polyp tracklet representations that outperform prior self-supervised and supervised baselines on retrieval, re-identification, size estimation, and histology tasks.
Neuro- computing423, 721–734 (2021)
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Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos
A noise-aware contrastive loss derived from temporal structure in colonoscopy videos produces polyp tracklet representations that outperform prior self-supervised and supervised baselines on retrieval, re-identification, size estimation, and histology tasks.