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This contribution presents a deep-learning method for extracting and fusingimage information acquired from different viewpoints, with the aim to producemore discriminant object features for the identification of the type of kidneystones seen in endoscopic images. The model was further improved with atwo-step transfer learning approach and by attention blocks to refine thelearned feature maps. Deep feature fusion strategies improved the results ofsingle view extraction backbone models by more than 6% in terms of accuracy ofthe kidney stones classification.

Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning