A confidence-weighted consistency loss between successive mini-batches improves polyp segmentation Dice by about one to three points over base training on several unseen colonoscopy datasets, but the method is an incremental variant of existing self-distillation.
A benchmark for endoluminal scene segmen- tation of colonoscopy images
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The Power of Certainty: How Confident Models Lead to Better Segmentation
A confidence-weighted consistency loss between successive mini-batches improves polyp segmentation Dice by about one to three points over base training on several unseen colonoscopy datasets, but the method is an incremental variant of existing self-distillation.