2 citations · 6 across the 12 of their papers we have counts for
12 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…
An FPGA smart camera implementation of segmentation models for drone wildfire imagery
Eduardo Guarduño-Martinez, Jorge Ciprian-Sanchez, Gerardo Valente +5
Wildfires represent one of the most relevant natural disasters worldwide, due to their impact on various societal and environmental levels. Thus, a significant amount of research h…
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…
A metric learning approach for endoscopic kidney stone identification
Jorge Gonzalez-Zapata, Francisco Lopez-Tiro, Elias Villalvazo-Avila +5
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 i…
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification
Mauricio Mendez-Ruiz, Jorge Gonzalez-Zapata, Ivan Reyes-Amezcua +4
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 levera…
Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction
Ricardo Espinosa, Carlos Axel Garcia-Vega, Gilberto Ochoa-Ruiz +2
This contribution shows how an appropriate image pre-processing can improve a deep-learning based 3D reconstruction of colon parts. The assumption is that, rather than global image…