Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis

Christodoulidis, Stergios; Anthimopoulos, Marios; Ebner, Lukas; Christe, Andreas; Mougiakakou, Stavroula (2017). Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis. IEEE journal of biomedical and health informatics, 21(1), pp. 76-84. Institute of Electrical and Electronics Engineers 10.1109/JBHI.2016.2636929

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Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis (CAD) systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Diabetes Technology
04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Christodoulidis, Stergios; Anthimopoulos, Marios; Ebner, Lukas; Christe, Andreas and Mougiakakou, Stavroula

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology
600 Technology > 620 Engineering

ISSN:

2168-2194

Publisher:

Institute of Electrical and Electronics Engineers

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

27 Dec 2016 13:23

Last Modified:

21 Sep 2017 13:12

Publisher DOI:

10.1109/JBHI.2016.2636929

URI:

https://boris.unibe.ch/id/eprint/92130

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