Zobrazeno 1 - 10
of 253
pro vyhledávání: '"Mendez-Vazquez A"'
Autor:
Reyes-Amezcua, Ivan, Rojas-Ruiz, Michael, Ochoa-Ruiz, Gilberto, Mendez-Vazquez, Andres, Daul, Christian
Deep learning developments have improved medical imaging diagnoses dramatically, increasing accuracy in several domains. Nonetheless, obstacles continue to exist because of the requirement for huge datasets and legal limitations on data exchange. A s
Externí odkaz:
http://arxiv.org/abs/2409.19934
Autor:
Reyes-Amezcua, Ivan, Espinosa, Ricardo, Daul, Christian, Ochoa-Ruiz, Gilberto, Mendez-Vazquez, Andres
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 EndoDepth benchmark, an evaluation framework designed to assess the ro
Externí odkaz:
http://arxiv.org/abs/2409.19930
Autor:
Flores-Araiza, Daniel, Lopez-Tiro, Francisco, Larose, Clément, Hinojosa, Salvador, Mendez-Vazquez, Andres, Gonzalez-Mendoza, Miguel, Ochoa-Ruiz, Gilberto, Daul, Christian
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 extraction process, while diminishing infection risks. Furthermore, such
Externí odkaz:
http://arxiv.org/abs/2409.12883
We propose a data-driven approach using a Restricted Boltzmann Machine (RBM) to solve the Schr\"odinger equation in configuration space. Traditional Configuration Interaction (CI) methods, while powerful, are computationally expensive due to the larg
Externí odkaz:
http://arxiv.org/abs/2409.06146
Autor:
Osorio, Sandra Leticia Juárez, Ruiz, Mayra Alejandra Rivera, Mendez-Vazquez, Andres, Rodriguez-Tello, Eduardo
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 point becomes a feature, transforming the problem into a multidimensi
Externí odkaz:
http://arxiv.org/abs/2404.15377
Autor:
Gonzalez-Zapata, Jorge, Lopez-Tiro, Francisco, Villalvazo-Avila, Elias, Flores-Araiza, Daniel, Hubert, Jacques, Mendez-Vazquez, Andres, Ochoa-Ruiz, Gilberto, Daul, Christian
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 if these DL approaches led to promising results, they are mainly approp
Externí odkaz:
http://arxiv.org/abs/2307.07046
Autor:
Mendez-Ruiz, Mauricio, Gonzalez-Zapata, Jorge, Reyes-Amezcua, Ivan, Flores-Araiza, Daniel, Lopez-Tiro, Francisco, Mendez-Vazquez, Andres, Ochoa-Ruiz, Gilberto
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 leverage the meta-learning approach, which is encompassed by episodic tasks
Externí odkaz:
http://arxiv.org/abs/2305.09062
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 were obtained by replacing the convolutions with wavelets. However, w
Externí odkaz:
http://arxiv.org/abs/2208.06882
Autor:
Reyes-Amezcua, Ivan, Flores-Araiza, Daniel, Ochoa-Ruiz, Gilberto, Mendez-Vazquez, Andres, Rodriguez-Tello, Eduardo
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 domain-knowledge which involves a time-consuming process. Thereby, aut
Externí odkaz:
http://arxiv.org/abs/2207.04010
Autor:
Gonzalez-Zapata, Jorge, Reyes-Amezcua, Ivan, Flores-Araiza, Daniel, Mendez-Ruiz, Mauricio, Ochoa-Ruiz, Gilberto, Mendez-Vazquez, Andres
Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning. However, they sometimes lack generalization properties because they are trained often using an inappropriate sample selection strategy or due to the difficul
Externí odkaz:
http://arxiv.org/abs/2206.02029