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A metric learning approach for endoscopic kidney stone identification
The proposed Guided Deep Metric Learning approach is based on a novel architecture which was designed to learn data representations in an improved way.
Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning
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.
Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies
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.
Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images
Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapses.
Comparing feature fusion strategies for Deep Learning-based kidney stone identification
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.
