1 citations · 1 across the 5 of their papers we have counts for
5 papers
Robust Federated Anomaly Detection Using Dual-Signal Autoencoders: Application to Kidney Stone Identification in Ureteroscopy
Ivan Reyes-Amezcua, Francisco Lopez-Tiro, Clément Larose +3
This work introduces Federated Adaptive Gain via Dual Signal Trust (FedAgain), a novel federated learning algorithm designed to enhance anomaly detection in medical imaging under d…
Vision Transformers for Kidney Stone Image Classification: A Comparative Study with CNNs
Ivan Reyes-Amezcua, Francisco Lopez-Tiro, Clement Larose +3
Kidney stone classification from endoscopic images is critical for personalized treatment and recurrence prevention. While convolutional neural networks (CNNs) have shown promise i…
Evaluation of Few-Shot Learning Methods for Kidney Stone Type Recognition in Ureteroscopy
Carlos Salazar-Ruiz, Francisco Lopez-Tiro, Ivan Reyes-Amezcua +3
Determining the type of kidney stones is crucial for prescribing appropriate treatments to prevent recurrence. Currently, various approaches exist to identify the type of kidney st…
Assessing the generalization performance of SAM for ureteroscopy scene understanding
Martin Villagrana, Francisco Lopez-Tiro, Clement Larose +2
The segmentation of kidney stones is regarded as a critical preliminary step to enable the identification of urinary stone types through machine- or deep-learning-based approaches.…
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…