Several Deep Learning (DL) methods have recently been proposed for an automated identification of kidney stones during an ureteroscopy to enable rapid therapeutic decisions. Even if these DL approaches led to promising results, they are mainly appropriate for kidney stone types for which numerous labelled data are available. However, only few labelled images are available for some rare kidney stone types. This contribution exploits Deep Metric Learning (DML) methods i) to handle such classes with few samples, ii) to generalize well to out of distribution samples, and iii) to cope better with new classes which are added to the database. The proposed Guided Deep Metric Learning approach is based on a novel architecture which was designed to learn data representations in an improved way. The solution was inspired by Few-Shot Learning (FSL) and makes use of a teacher-student approach. The teacher model (GEMINI) generates a reduced hypothesis space based on prior knowledge from the labeled data, and is used it as a guide to a student model (i.e., ResNet50) through a Knowledge Distillation scheme. Extensive tests were first performed on two datasets separately used for the recognition, namely a set of images acquired for the surfaces of the kidney stone fragments, and a set of images of the fragment sections. The proposed DML-approach improved the identification accuracy by 10% and 12% in comparison to DL-methods and other DML-approaches, respectively. Moreover, model embeddings from the two dataset types were merged in an organized way through a multi-view scheme to simultaneously exploit the information of surface and section fragments. Test with the resulting mixed model improves the identification accuracy by at least 3% and up to 30% with respect to DL-models and shallow machine learning methods, respectively.

翻译：近年来，多种深度学习（DL）方法被提出用于输尿管镜检查中自动识别肾结石，以支持快速治疗决策。尽管这些深度学习方法取得了有希望的结果，但它们主要适用于已有大量标注数据的肾结石类型。然而，对于某些罕见肾结石类型，可用标注图像很少。本研究利用深度度量学习（DML）方法：i）处理这些样本量少的类别，ii）良好泛化至分布外样本，iii）更好地应对新增至数据库的新类别。所提出的引导式深度度量学习方法基于一种新颖架构，该架构旨在以改进方式学习数据表征。该方案受小样本学习（FSL）启发，并采用师生模型框架。教师模型（GEMINI）基于标注数据的先验知识生成缩减后的假设空间，并通过知识蒸馏策略将其作为学生模型（如ResNet50）的引导。首先在两个分别用于识别的数据集上进行了广泛测试：一组为肾结石碎片表面采集的图像，另一组为碎片截面图像。与深度学习方法及其他深度度量学习方法相比，所提出的DML方法分别将识别准确率提升了10%和12%。此外，通过多视图方案将两类数据集的模型嵌入以结构化方式融合，以同时利用表面和截面碎片的信息。与深度学习模型及浅层机器学习方法相比，使用融合后的混合模型进行测试，识别准确率分别至少提升了3%和至多30%。