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Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis.

Stergios Christodoulidis, Marios Anthimopoulos, Lukas Ebner

    IEEE Journal of Biomedical and Health Informatics
    |January 24, 2017
    PubMed
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    Transfer learning improved deep convolutional neural network (CNN) performance for interstitial lung disease diagnosis by 2%. This method enhances medical image analysis by leveraging general texture classification knowledge for specialized lung tissue pattern recognition.

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Pulmonology

    Background:

    • Early diagnosis of interstitial lung diseases (ILDs) is critical but challenging due to overlapping clinical symptoms.
    • Computer-aided diagnosis (CAD) systems using CT image analysis aid in ILD diagnosis.
    • Previous work introduced a specific deep convolutional neural network (CNN) architecture for classifying lung tissue patterns.

    Purpose of the Study:

    • To improve the training methodology for a previously proposed CNN architecture for lung tissue pattern classification.
    • To investigate the efficacy of transfer learning from general texture classification to medical image analysis for ILDs.

    Main Methods:

    • Pre-training CNNs with the proposed architecture on six public texture databases.
    • Fine-tuning the pre-trained networks on specific lung tissue data.

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  • Ensembling the fine-tuned CNNs and compressing their fused knowledge back into a single network.

    • Main Results:

    • The transfer learning approach resulted in an absolute performance increase of approximately 2% for the CNN.
    • Demonstrated the effectiveness of knowledge transfer from general texture classification to medical image analysis.
    • Highlighted the significance of the training method in network performance, comparable to architectural design.

    Conclusions:

    • Transfer learning shows significant potential for enhancing medical image analysis tasks, specifically in ILD diagnosis.
    • The textural characteristics of lung pathologies are amenable to classification using deep learning.
    • Optimized training strategies, including transfer learning, are crucial for developing effective AI-driven diagnostic tools.