1 citations · 2 across the 19 of their papers we have counts for
5 papers · 1 filter
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…