1016 domain-specific pretraining of deep learning systems in gastrointestinal endoscopy improves performance over current state-of-the-art pretraining methods

Aims Pretraining might be more effective if the training data resemble the envisioned application. We investigated if pretraining on general endoscopic imagery results in a better performance of five existing AI systems with an application in gastro-intestinal endoscopy, compared to current state-of-the art pretraining approaches (i.e., supervised pretraining with ImageNet and semi-weakly supervised pretraining with the Billion-scale data set).