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：本研究报告提出了一种深度学习技术，用于提取并融合不同视角获取的图像信息，旨在生成更具判别力的物体特征，以识别内窥镜图像中可见的肾结石类型。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。与单一视角提取的骨干模型相比，深度特征融合策略在肾结石分类准确率上提升了超过6%。