Repositorio UVigo

Deep neural networks approaches for detecting and classifying colorectal polyps

Investigo Repository

Deep neural networks approaches for detecting and classifying colorectal polyps

Nogueira Rodríguez, Alba; Domínguez Carbajales, Rubén; López Fernández, Hugo; Iglesias, Águeda; Cubiella Fernández, Joaquín; Fernández Riverola, Florentino; Reboiro Jato, Miguel; González Peña, Daniel
 
DATE : 2021-01
UNIVERSAL IDENTIFIER : http://hdl.handle.net/11093/2802
UNESCO SUBJECT : 32 Ciencias Médicas ; 1203.04 Inteligencia Artificial ; 1203.20 Sistemas de Control Medico
DOCUMENT TYPE : article

ABSTRACT :

Deep Learning (DL) has attracted a lot of attention in the field of medical image analysis because of its higher performance in image classification when compared to previous state-of-the-art techniques. In addition, a recent meta-analysis found that the diagnostic performance of DL models is equivalent to that of health-care professionals. In this scenario, a lot of research using DL for polyp detection and classification have been published showing promising results in the last five years. Our work aims to review the most relevant studies from a technical point of view, focusing on the low-level details for the implementation of the DL models. To do so, this review analyzes the published research covering aspects like DL architectures, training strategies, data augmentation, transfer learning, or the features of the datasets used and their impact on the performance of the models. Additionally, comparative tables summarizing the main aspects analyzed in this review are publicly available at https://github.com/sing-group/deep-learning-colonoscopy.

Show full item record



Files in this item

Attribution 4.0 International Except where otherwise noted, this item's license is described as Attribution 4.0 International
2013 Universidade de Vigo, Todos los derechos reservados
Calidad So9001