# Assessing deep learning methods for the identification of kidney stones in endoscopic images

arXiv: https://arxiv.org/abs/2103.01146 · HTML: https://mlsanity.com/paper/2103.01146/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 1 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)

## Built on by (most cited first, 16 of 16)

- [Towards Automatic Recognition of Pure & Mixed Stones using Intraoperative Endoscopic Digital Images](https://mlsanity.com/paper/2105.10686/) (2021)
- [Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning](https://mlsanity.com/paper/2210.13654/) (2022)
- [Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies](https://mlsanity.com/paper/2211.02967/) (2022)
- [On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification](https://mlsanity.com/paper/2205.00895/) (2022)
- [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)
- [Comparing feature fusion strategies for Deep Learning-based kidney stone identification](https://mlsanity.com/paper/2206.00069/) (2022)
- [Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images](https://mlsanity.com/paper/2206.00252/) (2022)
- [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)
- [Causal Scoring Medical Image Explanations: A Case Study On Ex-vivo Kidney Stone Images](https://mlsanity.com/paper/2309.01921/) (2023)
- [Assessing the generalization performance of SAM for ureteroscopy scene understanding](https://mlsanity.com/paper/2505.17210/) (2025)
- [Evaluation of Few-Shot Learning Methods for Kidney Stone Type Recognition in Ureteroscopy](https://mlsanity.com/paper/2505.17921/) (2025)
- [Vision Transformers for Kidney Stone Image Classification: A Comparative Study with CNNs](https://mlsanity.com/paper/2508.13461/) (2025)
- [FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopy](https://mlsanity.com/paper/2603.19512/) (2026)

## Abstract (arXiv)

Knowing the type (i.e., the biochemical composition) of kidney stones is crucial to prevent relapses with an appropriate treatment. During ureteroscopies, kidney stones are fragmented, extracted from the urinary tract, and their composition is determined using a morpho-constitutional analysis. This procedure is time consuming (the morpho-constitutional analysis results are only available after some days) and tedious (the fragment extraction lasts up to an hour). Identifying the kidney stone type only with the in-vivo endoscopic images would allow for the dusting of the fragments, while the morpho-constitutional analysis could be avoided. Only few contributions dealing with the in vivo identification of kidney stones were published. This paper discusses and compares five classification methods including deep convolutional neural networks (DCNN)-based approaches and traditional (non DCNN-based) ones. Even if the best method is a DCCN approach with a precision and recall of 98% and 97% over four classes, this contribution shows that a XGBoost classifier exploiting well-chosen feature vectors can closely approach the performances of DCNN classifiers for a medical application with a limited number of annotated data.