# Comparing feature fusion strategies for Deep Learning-based kidney stone identification

arXiv: https://arxiv.org/abs/2206.00069 · HTML: https://mlsanity.com/paper/2206.00069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 4 of 4)

- [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)
- [Multi-view metric learning for multi-instance image classification](https://mlsanity.com/paper/1610.06671/) (2016)

## Built on by (most cited first, 1 of 1)

- [Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies](https://mlsanity.com/paper/2211.02967/) (2022)

## Abstract (arXiv)

This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints with the aim to produce more discriminant object features. Our approach was specifically designed to mimic the morpho-constitutional analysis used by urologists to visually classify kidney stones by inspecting the sections and surfaces of their fragments. Deep feature fusion strategies improved the results of single view extraction backbone models by more than 10% in terms of precision of the kidney stones classification.