4 citations · 4 across the 6 of their papers we have counts for
6 papers
FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation
Pablo Cesar Quihui-Rubio, Daniel Flores-Araiza, Miguel Gonzalez-Mendoza +2
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 ca…
Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores
Pablo Cesar Quihui-Rubio, Daniel Flores-Araiza, Gilberto Ochoa-Ruiz +2
This study focuses on comparing deep learning methods for the segmentation and quantification of uncertainty in prostate segmentation from MRI images. The aim is to improve the wor…
Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures
Pablo Cesar Quihui-Rubio, Gilberto Ochoa-Ruiz, Miguel Gonzalez-Mendoza +2
Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide. The main problem that specialists face during the dia…
Impact of loss function in Deep Learning methods for accurate retinal vessel segmentation
Daniela Herrera, Gilberto Ochoa-Ruiz, Miguel Gonzalez-Mendoza +1
The retinal vessel network studied through fundus images contributes to the diagnosis of multiple diseases not only found in the eye. The segmentation of this system may help the s…
Comparing Machine Learning based Segmentation Models on Jet Fire Radiation Zones
Carmina Pérez-Guerrero, Adriana Palacios, Gilberto Ochoa-Ruiz +3
Risk assessment is relevant in any workplace, however there is a degree of unpredictability when dealing with flammable or hazardous materials so that detection of fire accidents b…
Semi-automated labelling of medical images: benefits of a collaborative work in the evaluation of prostate cancer in MRI
Christian Mata, Alain Lalande, Paul Walker +2
Purpose: The goal of this study is to show the advantage of a collaborative work in the annotation and evaluation of prostate cancer tissues from T2-weighted MRI compared to the co…