no code implementations • 4 Sep 2023 • Pablo Cesar Quihui-Rubio, Daniel Flores-Araiza, Miguel Gonzalez-Mendoza, Christian Mata, Gilberto Ochoa-Ruiz
This contribution presents a deep learning method for the segmentation of prostate zones in MRI images based on U-Net using additive and feature pyramid attention modules, which can improve the workflow of prostate cancer detection and diagnosis.
no code implementations • 4 Sep 2023 • Eduardo Guarduño-Martinez, Jorge Ciprian-Sanchez, Gerardo Valente, Vazquez-Garcia, Gerardo Rodriguez-Hernandez, Adriana Palacios-Rosas, Lucile Rossi-Tisson, Gilberto Ochoa-Ruiz
Wildfires represent one of the most relevant natural disasters worldwide, due to their impact on various societal and environmental levels.
no code implementations • 9 Aug 2023 • Pablo Cesar Quihui-Rubio, Daniel Flores-Araiza, Gilberto Ochoa-Ruiz, Miguel Gonzalez-Mendoza, Christian Mata
This study focuses on comparing deep learning methods for the segmentation and quantification of uncertainty in prostate segmentation from MRI images.
no code implementations • 13 Jul 2023 • Jorge Gonzalez-Zapata, Francisco Lopez-Tiro, Elias Villalvazo-Avila, Daniel Flores-Araiza, Jacques Hubert, Andres Mendez-Vazquez, Gilberto Ochoa-Ruiz, Christian Daul
The proposed Guided Deep Metric Learning approach is based on a novel architecture which was designed to learn data representations in an improved way.
no code implementations • 15 May 2023 • Mauricio Mendez-Ruiz, Jorge Gonzalez-Zapata, Ivan Reyes-Amezcua, Daniel Flores-Araiza, Francisco Lopez-Tiro, Andres Mendez-Vazquez, Gilberto Ochoa-Ruiz
Few-shot learning is a challenging area of research that aims to learn new concepts with only a few labeled samples of data.
1 code implementation • 10 Apr 2023 • David Laines, Gissella Bejarano, Miguel Gonzalez-Mendoza, Gilberto Ochoa-Ruiz
We evaluated the effectiveness of our model on the Ankara University Turkish Sign Language (TSL) dataset, AUTSL, and a Mexican Sign Language (LSM) dataset.
1 code implementation • 8 Apr 2023 • Daniel Flores-Araiza, Francisco Lopez-Tiro, Jonathan El-Beze, Jacques Hubert, Miguel Gonzalez-Mendoza, Gilberto Ochoa-Ruiz, Christian Daul
Using PPs in the classification task enables case-based reasoning explanations for such output, thus making the model interpretable.
no code implementations • 6 Apr 2023 • Francisco Lopez-Tiro, Elias Villalvazo-Avila, Juan Pablo Betancur-Rengifo, Ivan Reyes-Amezcua, Jacques Hubert, Gilberto Ochoa-Ruiz, Christian Daul
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 features for the identification of the type of kidney stones seen in endoscopic images.
no code implementations • 6 Apr 2023 • Ricardo Espinosa, Carlos Axel Garcia-Vega, Gilberto Ochoa-Ruiz, Dominique Lamarque, Christian Daul
This contribution shows how an appropriate image pre-processing can improve a deep-learning based 3D reconstruction of colon parts.
no code implementations • 9 Nov 2022 • Rafael Martinez-Garcia-Peña, Mansoor Ali Teevno, Gilberto Ochoa-Ruiz, Sharib Ali
In this paper, we explore the domain generalisation technique to enable DL methods to be used in such scenarios.
no code implementations • 5 Nov 2022 • Elias Villalvazo-Avila, Francisco Lopez-Tiro, Jonathan El-Beze, Jacques Hubert, Miguel Gonzalez-Mendoza, Gilberto Ochoa-Ruiz, Christian Daul
Moreover, in comparison to the state-of-the-art, the fusion of the deep features improved the overall results up to 11% in terms of kidney stone classification accuracy.
no code implementations • 26 Oct 2022 • Axel Garcia-Vega, Ricardo Espinosa, Luis Ramirez-Guzman, Thomas Bazin, Luis Falcon-Morales, Gilberto Ochoa-Ruiz, Dominique Lamarque, Christian Daul
Endoscopy is the most widely used imaging technique for the diagnosis of cancerous lesions in hollow organs.
no code implementations • 24 Oct 2022 • Francisco Lopez-Tiro, Juan Pablo Betancur-Rengifo, Arturo Ruiz-Sanchez, Ivan Reyes-Amezcua, Jonathan El-Beze, Jacques Hubert, Michel Daudon, Gilberto Ochoa-Ruiz, Christian Daul
Finally, in comparison to models that are trained from scratch or by initializing ImageNet weights, the obtained results suggest that the two-step approach extracts features improving the identification of kidney stones in endoscopic images.
1 code implementation • 21 Aug 2022 • Mansoor Ali, Gilberto Ochoa-Ruiz, Sharib Ali
Therefore, in this paper we introduce a semi-supervised learning (SSL) framework in surgical tool detection paradigm which aims to mitigate the scarcity of training data and the data imbalance through a knowledge distillation approach.
no code implementations • 19 Jul 2022 • Pedro E. Chavarrias-Solanon, Mansoor Ali-Teevno, Gilberto Ochoa-Ruiz, Sharib Ali
In this work, we leverage deep learning to develop a framework to improve the localization of difficult to detect lesions and minimize the missed detection rate.
no code implementations • 19 Jul 2022 • Pablo Cesar Quihui-Rubio, Gilberto Ochoa-Ruiz, Miguel Gonzalez-Mendoza, Gerardo Rodriguez-Hernandez, Christian Mata
Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide.
1 code implementation • 8 Jul 2022 • Ivan Reyes-Amezcua, Daniel Flores-Araiza, Gilberto Ochoa-Ruiz, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello
Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets.
no code implementations • 6 Jul 2022 • Axel Garcia-Vega, Ricardo Espinosa, Gilberto Ochoa-Ruiz, Thomas Bazin, Luis Eduardo Falcon-Morales, Dominique Lamarque, Christian Daul
Endoscopy is the most widely used medical technique for cancer and polyp detection inside hollow organs.
no code implementations • 15 Jun 2022 • Pedro Esteban Chavarrias-Solano, Carlos Axel Garcia-Vega, Francisco Javier Lopez-Tiro, Gilberto Ochoa-Ruiz, Thomas Bazin, Dominique Lamarque, Christian Daul
Extensive experiments show the superiority, in terms of mean average precision, of the ensemble approach over the individual models and previous works in the state of the art.
no code implementations • 5 Jun 2022 • Carmina Pérez-Guerrero, Jorge Francisco Ciprián-Sánchez, Adriana Palacios, Gilberto Ochoa-Ruiz, Miguel Gonzalez-Mendoza, Vahid Foroughi, Elsa Pastor, Gerardo Rodriguez-Hernandez
The results suggest that it is possible to realistically replicate the results for experiments carried out using both visible and infrared cameras.
no code implementations • 4 Jun 2022 • Jorge Gonzalez-Zapata, Ivan Reyes-Amezcua, Daniel Flores-Araiza, Mauricio Mendez-Ruiz, Gilberto Ochoa-Ruiz, Andres Mendez-Vazquez
Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning.
no code implementations • 1 Jun 2022 • Daniela Herrera, Gilberto Ochoa-Ruiz, Miguel Gonzalez-Mendoza, Christian Mata
The best average of Hausdorff distance and mean square error were obtained using the Nested U-Net with the Dice loss function, which had an average of 6. 32 and 0. 0241 respectively.
no code implementations • 1 Jun 2022 • Daniel Flores-Araiza, Francisco Lopez-Tiro, Elias Villalvazo-Avila, Jonathan El-Beze, Jacques Hubert, Gilberto Ochoa-Ruiz, Christian Daul
Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapses.
no code implementations • 31 May 2022 • Elias Villalvazo-Avila, Francisco Lopez-Tiro, Daniel Flores-Araiza, Gilberto Ochoa-Ruiz, Jonathan El-Beze, Jacques Hubert, Christian Daul
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 features.
no code implementations • 21 Jan 2022 • Francisco Lopez-Tiro, Vincent Estrade, Jacques Hubert, Daniel Flores-Araiza, Miguel Gonzalez-Mendoza, Gilberto Ochoa-Ruiz, Christian Daul
This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculi types acquired with an endoscope during standard ureteroscopies.
no code implementations • 20 Jan 2022 • Carmina Pérez-Guerrero, Adriana Palacios, Gilberto Ochoa-Ruiz, Christian Mata, Joaquim Casal, Miguel Gonzalez-Mendoza, Luis Eduardo Falcón-Morales
This research work explores the application of deep learning models in an alternative approach that uses the semantic segmentation of jet fires flames to extract main geometrical attributes, relevant for fire risk assessments.
no code implementations • 9 Nov 2021 • Juan Carlos Angeles-Ceron, Gilberto Ochoa-Ruiz, Leonardo Chang, Sharib Ali
While accurate tracking of surgical instruments in real-time plays a crucial role in minimally invasive computer-assisted surgeries, it is a challenging task to achieve, mainly due to 1) complex surgical environment, and 2) model design with both optimal accuracy and speed.
1 code implementation • Applied Sciences 2021 • Jorge Francisco Ciprián-Sánchez, Gilberto Ochoa-Ruiz, Lucile Rossi, Frédéric Morandini
However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results.
no code implementations • 7 Jul 2021 • Carmina Pérez-Guerrero, Adriana Palacios, Gilberto Ochoa-Ruiz, Christian Mata, Miguel Gonzalez-Mendoza, Luis Eduardo Falcón-Morales
One such characterization would be the segmentation of different radiation zones within the flame, so this paper presents an exploratory research regarding several traditional computer vision and Deep Learning segmentation approaches to solve this specific problem.
no code implementations • 6 Jul 2021 • Mauricio Mendez-Ruiz, Ivan Garcia Jorge Gonzalez-Zapata, Gilberto Ochoa-Ruiz, Andres Mendez-Vazquez
This module helps to improve the accuracy performance by allowing the similarity function, given by the metric learning method, to have more discriminative features for the classification.
1 code implementation • 11 Apr 2021 • Cuauhtemoc Daniel Suarez-Ramirez, Miguel Gonzalez-Mendoza, Leonardo Chang-Fernandez, Gilberto Ochoa-Ruiz, Mario Alberto Duran-Vega
Current techniques for weight-updating use the same approaches as traditional Neural Networks (NNs) with the extra requirement of using an approximation to the derivative of the sign function - as it is the Dirac-Delta function - for back-propagation; thus, efforts are focused adapting full-precision techniques to work on BNNs.
no code implementations • 30 Mar 2021 • Juan Carlos Angeles Ceron, Leonardo Chang, Gilberto Ochoa-Ruiz, Sharib Ali
Image-based tracking of laparoscopic instruments plays a fundamental role in computer and robotic-assisted surgeries by aiding surgeons and increasing patient safety.