Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/69170

TítuloBone age assessment using general use convolutional neural networks
Autor(es)Pinheiro, Gonçalo João Pereira
Magalhães, Luís Gonzaga Mendes
Guevara, Miguel A.
Palavras-chaveMedical Imaging
X-rays
Computer-Aided Diagnosis (CAD)
Bone Age
Bone Age Assessment
Computer Vision
Deep Learning
Deep Neural Network
Convolutional Neural Networks
Data2019
EditoraIEEE
CitaçãoPinheiro, G., Magalhães, L., & Guevara, M. A. (2019, November). Bone age assessment using general use convolutional neural networks. In 2019 International Conference on Graphics and Interaction (ICGI) (pp. 80-85). IEEE.
Resumo(s)Deep Learning methods have been applied to different medical imaging analysis tasks like, e.g., lesion classification and tissue segmentation. Bone age assessment is traditionally performed on an x-ray of the non-dominant hand applying the Greulich and Pyle or the Tanner Whitehouse methods. In this work, we first have tested several state-of-the-art Convolution Neural Networks models for assessing bone age that previously has shown great results in general computer vision tasks. Based on these results, we have developed/optimized a new model, which is presented here. For this purpose, we used transfer learning methods and trained the selected networks from scratch achieving a 7.89-month error rate when assessing bone age in females.
TipoArtigo em ata de conferência
URIhttps://hdl.handle.net/1822/69170
ISBN978-1-7281-6379-6
e-ISBN978-1-7281-6378-9
DOI10.1109/ICGI47575.2019.8955014
Versão da editorahttps://ieeexplore.ieee.org/abstract/document/8955014
Arbitragem científicayes
AcessoAcesso restrito UMinho
Aparece nas coleções:CAlg - Artigos em livros de atas/Papers in proceedings

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