mlsanityData to 7 Oct 2026

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

arXiv 2205.00895 ↗First posted 2 May 2022cs.CVcited by 5

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Abstract (arXiv)

Deep learning has shown great promise in diverse areas of computer vision, such as image classification, object detection and semantic segmentation, among many others. However, as it has been repeatedly demonstrated, deep learning methods trained on a dataset do not generalize well to datasets from other domains or even to similar datasets, due to data distribution shifts. In this work, we propose the use of a meta-learning based few-shot learning approach to alleviate these problems. In order to demonstrate its efficacy, we use two datasets of kidney stones samples acquired with different endoscopes and different acquisition conditions. The results show how such methods are indeed capable 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 the same dataset, traditional Deep Learning (DL) methods attain only an accuracy of 45%.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 29 of its 52 references that are in the map. First posted on arXiv 2 May 2022.

Builds on

29 of 52 references.

References · 29

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17 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2024Deep Transfer Learning for Kidney Cancer Diagnosiscited by 3
  2. 2023A metric learning approach for endoscopic kidney stone identificationcited by 2
  3. 2023Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learningcited by 1
  4. 2025Assessing the generalization performance of SAM for ureteroscopy scene understandingcited by 0
  5. 2026Evaluating Few-Shot Pill Recognition Under Visual Domain Shiftcited by 0