9 citations · 33 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
On the in vivo recognition of kidney stones using machine learning
Francisco Lopez-Tiro, Vincent Estrade, Jacques Hubert +4
Determining the type of kidney stones allows urologists to prescribe a treatment to avoid recurrence of renal lithiasis. An automated in-vivo image-based classification method woul…