2 citations · 5 across the 11 of their papers we have counts for
11 papers
FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopy
Ivan Reyes-Amezcua, Francisco Lopez-Tiro, Clément Larose +3
The reliability of artificial intelligence (AI) in medical imaging critically depends on its robustness to heterogeneous and corrupted images acquired with diverse devices across d…
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…
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…
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…
Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning
Francisco Lopez-Tiro, Juan Pablo Betancur-Rengifo, Arturo Ruiz-Sanchez +6
Knowing the cause of kidney stone formation is crucial to establish treatments that prevent recurrence. There are currently different approaches for determining the kidney stone ty…
MACFE: A Meta-learning and Causality Based Feature Engineering Framework
Ivan Reyes-Amezcua, Daniel Flores-Araiza, Gilberto Ochoa-Ruiz +2
Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets. However, this process requires non-trivial…
Guided Deep Metric Learning
Jorge Gonzalez-Zapata, Ivan Reyes-Amezcua, Daniel Flores-Araiza +3
Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning. However, they sometimes lack generalization properties because they are trained often u…