# Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning

arXiv: https://arxiv.org/abs/2210.13654 · HTML: https://mlsanity.com/paper/2210.13654/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [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)
- [On the in vivo recognition of kidney stones using machine learning](https://mlsanity.com/paper/2201.08865/) (2022)
- [Finding Significant Features for Few-Shot Learning using Dimensionality Reduction](https://mlsanity.com/paper/2107.06992/) (2021)

## Built on by (most cited first, 10 of 10)

- [Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies](https://mlsanity.com/paper/2211.02967/) (2022)
- [Deep Transfer Learning for Kidney Cancer Diagnosis](https://mlsanity.com/paper/2408.04318/) (2024)
- [Classification of Endoscopy and Video Capsule Images using CNN-Transformer Model](https://mlsanity.com/paper/2408.10733/) (2024)
- [Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations](https://mlsanity.com/paper/2304.04077/) (2023)
- [A metric learning approach for endoscopic kidney stone identification](https://mlsanity.com/paper/2307.07046/) (2023)
- [Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition](https://mlsanity.com/paper/2409.19934/) (2024)
- [Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning](https://mlsanity.com/paper/2304.03193/) (2023)
- [Improving Prototypical Parts Abstraction for Case-Based Reasoning Explanations Designed for the Kidney Stone Type Recognition](https://mlsanity.com/paper/2409.12883/) (2024)
- [Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition](https://mlsanity.com/paper/2409.13409/) (2024)
- [Vision Transformers for Kidney Stone Image Classification: A Comparative Study with CNNs](https://mlsanity.com/paper/2508.13461/) (2025)

## Abstract (arXiv)

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.