  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.
