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Deep learning has shown great promise in diverse areas of computer vision,such as image classification, object detection and semantic segmentation, amongmany others. However, as it has been repeatedly demonstrated, deep learningmethods trained on a dataset do not generalize well to datasets from otherdomains or even to similar datasets, due to data distribution shifts. In thiswork, we propose the use of a meta-learning based few-shot learning approach toalleviate these problems. In order to demonstrate its efficacy, we use twodatasets of kidney stones samples acquired with different endoscopes anddifferent acquisition conditions. The results show how such methods are indeedcapable of handling domain-shifts by attaining an accuracy of 74.38% and 88.52%in the 5-way 5-shot and 5-way 20-shot settings respectively. Instead, in thesame dataset, traditional Deep Learning (DL) methods attain only an accuracy of45%.

On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification