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：本文提出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，旨在为内镜图像中肾结石类型的识别生成更具判别力的目标特征。该模型通过两步迁移学习策略和注意力模块进一步优化，以精炼学习到的特征图。深度特征融合策略使肾结石分类的准确率相较于单视角特征提取骨干模型提升了6%以上。