3 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…