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

翻译：本文介绍了一种深度学习方法，用于提取和融合从不同视角获取的图像信息，旨在产生更有辨别力的对象特征，以识别内镜图像中所见的肾结石类型。该模型采用两步式迁移学习方法和注意力机制进行进一步改进，以优化学习到的特征图。深度特征融合策略在肾结石分类的准确性方面比单视图提取骨干模型提高了超过6％。