1 citations · 2 across the 6 of their papers we have counts for
6 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…
Causal Scoring Medical Image Explanations: A Case Study On Ex-vivo Kidney Stone Images
Armando Villegas-Jimenez, Daniel Flores-Araiza, Francisco Lopez-Tiro +1
On the promise that if human users know the cause of an output, it would enable them to grasp the process responsible for the output, and hence provide understanding, many explaina…
A metric learning approach for endoscopic kidney stone identification
Jorge Gonzalez-Zapata, Francisco Lopez-Tiro, Elias Villalvazo-Avila +5
Several Deep Learning (DL) methods have recently been proposed for an automated identification of kidney stones during an ureteroscopy to enable rapid therapeutic decisions. Even i…
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification
Mauricio Mendez-Ruiz, Jorge Gonzalez-Zapata, Ivan Reyes-Amezcua +4
Few-shot learning is a challenging area of research that aims to learn new concepts with only a few labeled samples of data. Recent works based on metric-learning approaches levera…
Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations
Daniel Flores-Araiza, Francisco Lopez-Tiro, Jonathan El-Beze +4
Identifying the type of kidney stones can allow urologists to determine their cause of formation, improving the prescription of appropriate treatments to diminish future relapses.…