2 citations · 5 across the 5 of their papers we have counts for
5 papers
A metric learning approach for endoscopic kidney stone identification
Jorge Gonzalez-Zapata, Francisco Lopez-Tiro, Elias Villalvazo-Avila +5
Several Deep Learning (DL) methods have recently been proposed for an automated identification of kidney stones during an ureteroscopy to enable rapid therapeutic decisions. Even i…
Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning
Francisco Lopez-Tiro, Elias Villalvazo-Avila, Juan Pablo Betancur-Rengifo +4
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 f…
Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies
Elias Villalvazo-Avila, Francisco Lopez-Tiro, Jonathan El-Beze +4
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 endoscop…
Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images
Daniel Flores-Araiza, Francisco Lopez-Tiro, Elias Villalvazo-Avila +4
Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapse…
Comparing feature fusion strategies for Deep Learning-based kidney stone identification
Elias Villalvazo-Avila, Francisco Lopez-Tiro, Daniel Flores-Araiza +4
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 fe…