1 citations · 2 across the 19 of their papers we have counts for
20 papers
Efficient 3D plant segmentation through multiscale graph learning and optimized geometric processing
Jesus Ivan Ruiz-Martinez, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello
Plant organ segmentation on compact computing platforms requires accurate predictions and affordable geometric preparation. We develop and evaluate a multiscale edge based graph at…
Empirical Characterization of Learning Geometry in Hybrid Quantum Forecasting Models
Sandra Leticia Juárez-Osorio, Jorge I. Hernandez-Martinez, Jesus Ivan Ruiz-Martinez +2
We characterize the learning dynamics of a compact hybrid quantum forecasting model through comparison with a structurally aligned classical baseline. Using stationary harmonic-mix…
SRGAN-CKAN: Expressive Super-Resolution with Nonlinear Functional Operators under Minimal Resources
Roberto Isai Navaro-Aviña, Eduardo Said Merin-Martinez, Andres Mendez-Vazquez +1
Single-Image Super-Resolution (SISR) aims to reconstruct a High-Resolution (HR) image from a Low-Resolution (LR) observation, a fundamentally ill-posed problem where high-frequency…
LTBs-KAN: Linear-Time B-splines Kolmogorov-Arnold Networks
Eduardo Said Merin-Martinez, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello
Kolmogorov-Arnold Networks (KANs) are a recent neural network architecture offering an alternative to Multilayer Perceptrons (MLPs) with improved explainability and expressibility.…
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…
Graph-Based Deep Learning for Component Segmentation of Maize Plants
J. I. Ruiz-Martinez, A. Mendez-Vazquez, E. Rodriguez-Tello
In precision agriculture, one of the most important tasks when exploring crop production is identifying individual plant components. There are several attempts to accomplish this t…
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…
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…
Configuration Interaction Guided Sampling with Interpretable Restricted Boltzmann Machine
Jorge I. Hernandez-Martinez, Andres Mendez-Vazquez, Gerardo Rodriguez-Hernandez +1
We propose a data-driven approach using a Restricted Boltzmann Machine (RBM) to solve the Schrödinger equation in configuration space. Traditional Configuration Interaction (CI) me…
Fourier Series Guided Design of Quantum Convolutional Neural Networks for Enhanced Time Series Forecasting
Sandra Leticia Juárez Osorio, Mayra Alejandra Rivera Ruiz, Andres Mendez-Vazquez +1
In this study, we apply 1D quantum convolution to address the task of time series forecasting. By encoding multiple points into the quantum circuit to predict subsequent data, each…
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…
CoShNet: A Hybrid Complex Valued Neural Network using Shearlets
Manny Ko, Ujjawal K. Panchal, Héctor Andrade-Loarca +1
In a hybrid neural network, the expensive convolutional layers are replaced by a non-trainable fixed transform with a great reduction in parameters. In previous works, good results…
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…
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…
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction
Mauricio Mendez-Ruiz, Ivan Garcia Jorge Gonzalez-Zapata, Gilberto Ochoa-Ruiz +1
Few-shot learning is a relatively new technique that specializes in problems where we have little amounts of data. The goal of these methods is to classify categories that have not…