HIGHLIGHTS
SUMMARY
The last decade has seen a significant growth in supervised deep-learning (DL) methods for colonoscopy with automated feature learning from raw training images for prediction. Readers with interest in deeplearning methods for polyp image detection, polyp region localization and segmentation prior to 2020 are referred to. Training images tend to represent optimal conditions, e_g, a picture with a clean colon in perfect focus. How well does the model pre-trained on small datasets under optimal conditions generalize to real-world data under sub-optimal conditions e_g, polyps partially occluded with feces? The proposed . . .
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