A deep convolutional neural network architecture for interstitial lung disease pattern classification.
Interstitial lung disease (ILD) refers to a group of various abnormal inflammations of lung tissues and early diagnosis of these disease patterns is crucial for the treatment. Yet it is difficult to make an accurate diagnosis due to the similarity among the clinical manifestations of these diseases....
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 4; pp. 725 - 738 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142718784&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142718784 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2020 vid: 58 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142718784 142718784 NLM31965407 10.1007/s11517-019-02111-w NLM31965407 142718784 ppf: 725 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A deep convolutional neural network architecture for interstitial lung disease pattern classification. aug: au: Huang, Sheng Lee, Feifei Miao, Ran Si, Qin Lu, Chaowen Chen, Qiu affil: Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China sug: subj: Lung Diseases, Interstitial Diagnosis, Computer Assisted Methods Resource Databases Tomography, X-Ray Computed Scales ab: Interstitial lung disease (ILD) refers to a group of various abnormal inflammations of lung tissues and early diagnosis of these disease patterns is crucial for the treatment. Yet it is difficult to make an accurate diagnosis due to the similarity among the clinical manifestations of these diseases. In order to assist the radiologists, computer-aided diagnosis systems have been developed. Besides, the potential of deep convolutional neural networks (CNNs) is also expected to exert on the medical image analysis in recent years. In this paper, we design a new deep convolutional neural network (CNN) architecture to achieve the classification task of ILD patterns. Furthermore, we also propose a novel two-stage transfer learning (TSTL) method to deal with the problem of the lack of training data, which leverages the knowledge learned from sufficient textural source data and auxiliary unlabeled lung CT data to the target domain. We adopt the unsupervised manner to learn the unlabeled data, by which the objective function composed of the prediction confidence and mutual information are optimized. The experimental results show that our proposed CNN architecture achieves desirable performance and outperforms most of the state-of-the-art ones. The comparative analysis demonstrates the promising feasibility and advantages of the proposed two-stage transfer learning strategy as well as the potential of the knowledge learning from lung CT data. Graphical Abstract The framework of the proposed two-stage transfer learning method. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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