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Proceedings Paper

Unsupervised and semi-supervised learning for efficient opacity annotation of diffuse lung diseases
Author(s): Shingo Mabu; Shoji Kido; Yasushi Hirano; Takashi Kuremoto
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Paper Abstract

Research on computer-aided diagnosis (CAD) for medical images using machine learning has been actively conducted. However, machine learning, especially deep learning, requires a large number of training data with annotations. Deep learning often requires thousands of training data, but it is tough work for radiologists to give normal and abnormal labels to many images. In this research, aiming the efficient opacity annotation of diffuse lung diseases, unsupervised and semi-supervised opacity annotation algorithms are introduced. Unsupervised learning makes clusters of opacities based on the features of the images without using any opacity information, and semi-supervised learning efficiently uses the small number of training data with annotation for training classifiers. The performance evaluation is carried out by the classification of six kinds of opacities of diffuse lung diseases: consolidation, ground-glass opacity, honeycombing, emphysema, nodular and normal, and the effectiveness of the methods is clarified.

Paper Details

Date Published: 27 March 2019
PDF: 6 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110501D (27 March 2019); doi: 10.1117/12.2519929
Show Author Affiliations
Shingo Mabu, Yamaguchi Univ. (Japan)
Shoji Kido, Yamaguchi Univ. (Japan)
Yasushi Hirano, Yamaguchi Univ. (Japan)
Takashi Kuremoto, Yamaguchi Univ. (Japan)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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