Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/69170
Título: | Bone age assessment using general use convolutional neural networks |
Autor(es): | Pinheiro, Gonçalo João Pereira Magalhães, Luís Gonzaga Mendes Guevara, Miguel A. |
Palavras-chave: | Medical Imaging X-rays Computer-Aided Diagnosis (CAD) Bone Age Bone Age Assessment Computer Vision Deep Learning Deep Neural Network Convolutional Neural Networks |
Data: | 2019 |
Editora: | IEEE |
Citação: | Pinheiro, 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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/69170 |
ISBN: | 978-1-7281-6379-6 |
e-ISBN: | 978-1-7281-6378-9 |
DOI: | 10.1109/ICGI47575.2019.8955014 |
Versão da editora: | https://ieeexplore.ieee.org/abstract/document/8955014 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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08955014.pdf Acesso restrito! | 194,85 kB | Adobe PDF | Ver/Abrir |