Evaluation of Few-Shot Learning Methods for Kidney Stone Type Recognition in Ureteroscopy
Journal:
arXiv
Published Date:
May 23, 2025
Abstract
Determining the type of kidney stones is crucial for prescribing appropriate
treatments to prevent recurrence. Currently, various approaches exist to
identify the type of kidney stones. However, obtaining results through the
reference ex vivo identification procedure can take several weeks, while in
vivo visual recognition requires highly trained specialists. For this reason,
deep learning models have been developed to provide urologists with an
automated classification of kidney stones during ureteroscopies. Nevertheless,
a common issue with these models is the lack of training data. This
contribution presents a deep learning method based on few-shot learning, aimed
at producing sufficiently discriminative features for identifying kidney stone
types in endoscopic images, even with a very limited number of samples. This
approach was specifically designed for scenarios where endoscopic images are
scarce or where uncommon classes are present, enabling classification even with
a limited training dataset. The results demonstrate that Prototypical Networks,
using up to 25% of the training data, can achieve performance equal to or
better than traditional deep learning models trained with the complete dataset.
