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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.
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.
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