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Found 6 papers, 1 papers with code
Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations
Using PPs in the classification task enables case-based reasoning explanations for such output, thus making the model interpretable.
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.
Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning
Finally, in comparison to models that are trained from scratch or by initializing ImageNet weights, the obtained results suggest that the two-step approach extracts features improving the identification of kidney stones in endoscopic images.
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.
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification
Deep learning has shown great promise in diverse areas of computer vision, such as image classification, object detection and semantic segmentation, among many others.
