M
Miguel Angel Guevara Lopez
Researcher at University of Aveiro
Publications - 31
Citations - 1750
Miguel Angel Guevara Lopez is an academic researcher from University of Aveiro. The author has contributed to research in topics: Mammography & Mutant. The author has an hindex of 15, co-authored 31 publications receiving 1497 citations. Previous affiliations of Miguel Angel Guevara Lopez include University of Alcalá & Spanish National Research Council.
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Journal ArticleDOI
Representation learning for mammography mass lesion classification with convolutional neural networks
John Arevalo,Fabio A. González,Raúl Ramos-Pollán,José Luís Oliveira,Miguel Angel Guevara Lopez +4 more
TL;DR: An innovative representation learning framework for breast cancer diagnosis in mammography that integrates deep learning techniques to automatically learn discriminative features avoiding the design of specific hand-crafted image-based feature detectors is described.
Journal ArticleDOI
Oxylipins Produced by the 9-Lipoxygenase Pathway in Arabidopsis Regulate Lateral Root Development and Defense Responses through a Specific Signaling Cascade
Tamara Vellosillo,Marta Martínez,Miguel Angel Guevara Lopez,Jorge Vicente,Tomás Cascón,Liam Dolan,Mats Hamberg,Carmen Castresana +7 more
TL;DR: Findings that noxy2 displayed altered root development, enhanced susceptibility to Pseudomonas, and reduced the activation of 9-HOT–responding genes are consistent with mechanistic links among these processes and suggests that oxylipins from the 9-LOX pathway function in cell wall modifications required for lateral root development and pathogen arrest.
Journal ArticleDOI
Controlling hormone signaling is a plant and pathogen challenge for growth and survival
TL;DR: Results indicate that hormone signaling is a relevant component in plant-pathogen interactions, and that the ability to dictate hormonal directionality is critical to the outcome of an interaction.
Proceedings ArticleDOI
Convolutional neural networks for mammography mass lesion classification
John Arevalo,Fabio A. González,Raúl Ramos-Pollán,José Luís Oliveira,Miguel Angel Guevara Lopez +4 more
TL;DR: This work presents an evaluation of convolutional neural networks to learn features for mammography mass lesions before feeding them to a classification stage, and Experimental results showed that this approach is a suitable strategy outperforming the state-of-the-art representation.
Journal ArticleDOI
An evaluation of image descriptors combined with clinical data for breast cancer diagnosis
TL;DR: A new descriptor based on the divergence of the gradient (HGD) was demonstrated to be a feasible predictor of breast masses’ diagnosis, demonstrating promising capabilities to describe masses.