  Knowing the cause of kidney stone formation is crucial to establish
treatments that prevent recurrence. There are currently different approaches
for determining the kidney stone type. However, the reference ex-vivo
identification procedure can take up to several weeks, while an in-vivo visual
recognition requires highly trained specialists. Machine learning models have
been developed to provide urologists with an automated classification of kidney
stones during an ureteroscopy; however, there is a general lack in terms of
quality of the training data and methods. In this work, a two-step transfer
learning approach is used to train the kidney stone classifier. The proposed
approach transfers knowledge learned on a set of images of kidney stones
acquired with a CCD camera (ex-vivo dataset) to a final model that classifies
images from endoscopic images (ex-vivo dataset). The results show that learning
features from different domains with similar information helps to improve the
performance of a model that performs classification in real conditions (for
instance, uncontrolled lighting conditions and blur). Finally, in comparison to
models that are trained from scratch or by initializing ImageNet weights, the
obtained results suggest that the two-step approach extracts features improving
the identification of kidney stones in endoscopic images.
