Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning
Not on the map. Why?
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 for the identification of the type of kidney stones seen in endoscopic images. The model was further improved with a two-step transfer learning approach and by attention blocks to refine the learned feature maps. Deep feature fusion strategies improved the results of single view extraction backbone models by more than 6% in terms of accuracy of the kidney stones classification.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 32 references that are in the map. First posted on arXiv 6 Apr 2023.
Builds on
8 of 32 references.
References · 8
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
- 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
- 2021Assessing deep learning methods for the identification of kidney stones in endoscopic imagescited by 16
- 2021Towards Automatic Recognition of Pure & Mixed Stones using Intraoperative Endoscopic Digital Imagescited by 15
- 2022On the in vivo recognition of kidney stones using machine learningcited by 14
- 2022Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learningcited by 10
- 2022Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategiescited by 6
- 2022On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classificationcited by 5
Built on by
Citing papers per year: 2024: 1.
1 paper.