Fig. 2: The image-based performance of AI-doscopist on Dataset B under different training schemes. | npj Digital Medicine

Fig. 2: The image-based performance of AI-doscopist on Dataset B under different training schemes.

From: AI-doscopist: a real-time deep-learning-based algorithm for localising polyps in colonoscopy videos with edge computing devices

Fig. 2

a the Receiver Operating Characteristic curves and b the Precision–Recall curves. In Training Scheme a, AI-doscopist learnt only the spatial features from a random subset of 33,819 original colonoscopy images. In Scheme b, the training set was enlarged to a random subset of 119,703 original colonoscopy and non-medical images. In Scheme c, AI-doscopist learnt both the spatial and temporal features from a random subset of 119,703 original colonoscopy and non-medical images. In Training Scheme d, the spatial and temporal features were learnt from a larger, random subset of 191,493 colonoscopy and non-medical images. A total of 34,469 images were used for validation in each case.

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