4 citations · 16 across the 26 of their papers we have counts for
26 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…
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.…
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…
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 learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction
Ricardo Espinosa, Carlos Axel Garcia-Vega, Gilberto Ochoa-Ruiz +2
This contribution shows how an appropriate image pre-processing can improve a deep-learning based 3D reconstruction of colon parts. The assumption is that, rather than global image…
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.…
Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning
Francisco Lopez-Tiro, Elias Villalvazo-Avila, Juan Pablo Betancur-Rengifo +4
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 f…
Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies
Elias Villalvazo-Avila, Francisco Lopez-Tiro, Jonathan El-Beze +4
This contribution presents a deep learning method for the extraction and fusion of information relating to kidney stone fragments acquired from different viewpoints of the endoscop…
Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging
Axel Garcia-Vega, Ricardo Espinosa, Luis Ramirez-Guzman +5
Endoscopy is the most widely used imaging technique for the diagnosis of cancerous lesions in hollow organs. However, endoscopic images are often affected by illumination artefacts…
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…
A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models
Axel Garcia-Vega, Ricardo Espinosa, Gilberto Ochoa-Ruiz +4
Endoscopy is the most widely used medical technique for cancer and polyp detection inside hollow organs. However, images acquired by an endoscope are frequently affected by illumin…
Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams
Pedro Esteban Chavarrias-Solano, Carlos Axel Garcia-Vega, Francisco Javier Lopez-Tiro +4
In this contribution we use an ensemble deep-learning method for combining the prediction of two individual one-stage detectors (i.e., YOLOv4 and Yolact) with the aim to detect art…
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…
Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images
Daniel Flores-Araiza, Francisco Lopez-Tiro, Elias Villalvazo-Avila +4
Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapse…
Comparing feature fusion strategies for Deep Learning-based kidney stone identification
Elias Villalvazo-Avila, Francisco Lopez-Tiro, Daniel Flores-Araiza +4
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 fe…