1 citations · 1 across the 5 of their papers we have counts for
5 papers
Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition
Ivan Reyes-Amezcua, Michael Rojas-Ruiz, Gilberto Ochoa-Ruiz +2
Deep learning developments have improved medical imaging diagnoses dramatically, increasing accuracy in several domains. Nonetheless, obstacles continue to exist because of the req…
EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth Prediction
Ivan Reyes-Amezcua, Ricardo Espinosa, Christian Daul +2
Accurate depth estimation in endoscopy is vital for successfully implementing computer vision pipelines for various medical procedures and CAD tools. In this paper, we present the…
Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition
Ruben Gonzalez-Perez, Francisco Lopez-Tiro, Ivan Reyes-Amezcua +6
Currently, the Morpho-Constitutional Analysis (MCA) is the de facto approach for the etiological diagnosis of kidney stone formation, and it is an important step for establishing p…
Improving Prototypical Parts Abstraction for Case-Based Reasoning Explanations Designed for the Kidney Stone Type Recognition
Daniel Flores-Araiza, Francisco Lopez-Tiro, Clément Larose +5
The in-vivo identification of the kidney stone types during an ureteroscopy would be a major medical advance in urology, as it could reduce the time of the tedious renal calculi ex…
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification
Mauricio Mendez-Ruiz, Francisco Lopez-Tiro, Jonathan El-Beze +5
Deep learning has shown great promise in diverse areas of computer vision, such as image classification, object detection and semantic segmentation, among many others. However, as…