期刊
MEDICAL IMAGE ANALYSIS
卷 57, 期 -, 页码 237-248出版社
ELSEVIER
DOI: 10.1016/j.media.2019.07.004
关键词
Lung nodule classification; Semi-supervised learning; Adversarial learning; Deep learning
类别
资金
- National Natural Science Foundation of China [61771397]
- Science and Technology Innovation Committee of Shenzhen Municipality, China [JCYJ20180306171334997]
- Synergy Innovation Foundation of the University and Enterprise for Graduate Students in Northwestern Polytechnical University [XQ201911]
- Project for Graduate Innovation team of Northwestern Polytechnical University
Classification of benign-malignant lung nodules on chest CT is the most critical step in the early detection of lung cancer and prolongation of patient survival. Despite their success in image classification, deep convolutional neural networks (DCNNs) always require a large number of labeled training data, which are not available for most medical image analysis applications due to the work required in image acquisition and particularly image annotation. In this paper, we propose a semi-supervised adversarial classification (SSAC) model that can be trained by using both labeled and unlabeled data for benign-malignant lung nodule classification. This model consists of an adversarial autoencoder-based unsupervised reconstruction network R, a supervised classification network C, and learnable transition layers that enable the adaption of the image representation ability learned by R to C. The SSAC model has been extended to the multi-view knowledge-based collaborative learning, aiming to employ three SSACs to characterize each nodule's overall appearance, heterogeneity in shape and texture, respectively, and to perform such characterization on nine planar views. The MK-SSAC model has been evaluated on the benchmark LIDC-IDRI dataset and achieves an accuracy of 92.53% and an AUC of 95.81%, which are superior to the performance of other lung nodule classification and semi-supervised learning approaches. (C) 2019 Elsevier B.V. All rights reserved.
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