# Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies

arXiv: https://arxiv.org/abs/2211.02967 · HTML: https://mlsanity.com/paper/2211.02967/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 5 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Learn To Pay Attention](https://mlsanity.com/paper/1804.02391/) (2018)
- [High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks](https://mlsanity.com/paper/1703.07047/) (2017)
- [Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions](https://mlsanity.com/paper/2107.11508/) (2021)
- [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)
- [Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning](https://mlsanity.com/paper/2210.13654/) (2022)
- [Multi-view metric learning for multi-instance image classification](https://mlsanity.com/paper/1610.06671/) (2016)
- [Comparing feature fusion strategies for Deep Learning-based kidney stone identification](https://mlsanity.com/paper/2206.00069/) (2022)

## Built on by (most cited first, 6 of 6)

- [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)
- [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)
- [Multi-view learning for automatic classification of multi-wavelength auroral images](https://mlsanity.com/paper/2311.02947/) (2023)
- [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)

This contribution presents a deep learning method for the extraction and fusion of information relating to kidney stone fragments acquired from different viewpoints of the endoscope. Surface and section fragment images are jointly used during the training of the classifier to improve the discrimination power of the features by adding attention layers at the end of each convolutional block. This approach is specifically designed to mimic the morpho-constitutional analysis performed in ex-vivo by biologists to visually identify kidney stones by inspecting both views. The addition of attention mechanisms to the backbone improved the results of single view extraction backbones by 4% on average. Moreover, in comparison to the state-of-the-art, the fusion of the deep features improved the overall results up to 11% in terms of kidney stone classification accuracy.