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Proceedings Paper

Weak supervision in convolutional neural network for semantic segmentation of diffuse lung diseases using partially annotated dataset
Author(s): Yuki Suzuki; Kazuki Yamagata; Masahiro Yanagawa; Shoji Kido; Noriyuki Tomiyama
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Paper Abstract

Computer-aided diagnosis system for diffuse lung diseases (DLDs) is necessary for the objective assessment of the lung diseases. In this paper, we develop semantic segmentation model for 5 kinds of DLDs. DLDs considered in this work are consolidation, ground glass opacity, honeycombing, emphysema, and normal. Convolutional neural network (CNN) is one of the most promising technique for semantic segmentation among machine learning algorithms. While creating annotated dataset for semantic segmentation is laborious and time consuming, creating partially annotated dataset, in which only one chosen class is annotated for each image, is easier since annotators only need to focus on one class at a time during the annotation task. In this paper, we propose a new weak supervision technique that effectively utilizes partially annotated dataset. The experiments using partially annotated dataset composed 372 CT images demonstrated that our proposed technique significantly improved segmentation accuracy.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142A (16 March 2020); doi: 10.1117/12.2548930
Show Author Affiliations
Yuki Suzuki, Osaka Univ. (Japan)
Kazuki Yamagata, Osaka Univ. (Japan)
Masahiro Yanagawa, Osaka Univ. (Japan)
Shoji Kido, Osaka Univ. (Japan)
Noriyuki Tomiyama, Osaka Univ. (Japan)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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