Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data.
Comput Med Imaging Graph
; 57: 4-9, 2017 04.
Article
em En
| MEDLINE
| ID: mdl-27475279
ABSTRACT
In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, and our proposed scheme only requires a small portion of labeled data in training set. Four modules were included in the diagnosis system data weighing, feature selection, dividing co-training data labeling, and CNN. 3158 region of interests (ROIs) with each containing a mass extracted from 1874 pairs of mammogram images were used for this study. Among them 100 ROIs were treated as labeled data while the rest were treated as unlabeled. The area under the curve (AUC) observed in our study was 0.8818, and the accuracy of CNN is 0.8243 using the mixed labeled and unlabeled data.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
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Mamografia
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Diagnóstico por Computador
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Redes Neurais de Computação
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Aprendizado de Máquina Supervisionado
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
Limite:
Adult
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Aged
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Female
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Humans
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Middle aged
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article