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Bone age assessment using general use convolutional neural networks

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Resumo: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.
Autores principais:Pinheiro, Gonçalo João Pereira
Outros Autores:Magalhães, Luís Gonzaga Mendes; Guevara, Miguel A.
Assunto:Medical Imaging X-rays Computer-Aided Diagnosis (CAD) Bone Age Bone Age Assessment Computer Vision Deep Learning Deep Neural Network Convolutional Neural Networks Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Engenharia e Tecnologia::Engenharia Médica
Ano:2019
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso restrito
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho

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