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

arXiv: https://arxiv.org/abs/2205.00895 · HTML: https://mlsanity.com/paper/2205.00895/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 52 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 29 of 29)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Exploring Generalization in Deep Learning](https://mlsanity.com/paper/1706.08947/) (2017)
- [Contrastive Adaptation Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1901.00976/) (2019)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Using Deep Learning for Image-Based Plant Disease Detection](https://mlsanity.com/paper/1604.03169/) (2016)
- [Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift](https://mlsanity.com/paper/2003.04475/) (2020)
- [Few-Shot Image Recognition by Predicting Parameters from Activations](https://mlsanity.com/paper/1706.03466/) (2017)
- [Finding Task-Relevant Features for Few-Shot Learning by Category Traversal](https://mlsanity.com/paper/1905.11116/) (2019)
- [Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder](https://mlsanity.com/paper/2108.05028/) (2021)
- [Self-Supervised Learning For Few-Shot Image Classification](https://mlsanity.com/paper/1911.06045/) (2019)
- [Few-Shot Learning with Metric-Agnostic Conditional Embeddings](https://mlsanity.com/paper/1802.04376/) (2018)
- [Assessing deep learning methods for the identification of kidney stones in endoscopic images](https://mlsanity.com/paper/2103.01146/) (2021)
- [Towards Automatic Recognition of Pure & Mixed Stones using Intraoperative Endoscopic Digital Images](https://mlsanity.com/paper/2105.10686/) (2021)
- [A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging](https://mlsanity.com/paper/1904.01638/) (2019)
- [A Broader Study of Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/1912.07200/) (2019)
- [Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures](https://mlsanity.com/paper/2103.15997/) (2021)

## Built on by (most cited first, 5 of 5)

- [Deep Transfer Learning for Kidney Cancer Diagnosis](https://mlsanity.com/paper/2408.04318/) (2024)
- [A metric learning approach for endoscopic kidney stone identification](https://mlsanity.com/paper/2307.07046/) (2023)
- [Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning](https://mlsanity.com/paper/2304.03193/) (2023)
- [Assessing the generalization performance of SAM for ureteroscopy scene understanding](https://mlsanity.com/paper/2505.17210/) (2025)
- [Evaluating Few-Shot Pill Recognition Under Visual Domain Shift](https://mlsanity.com/paper/2603.10833/) (2026)

## 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%.