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A few-shot learning-based eye diseases screening method.

Z-K Han1, H Xing, B Yang

  • 1Zhejiang University, Hangzhou, Zhejiang, China. seven7@zju.edu.cn.

European Review for Medical and Pharmacological Sciences
|December 16, 2022
PubMed
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This study introduces a novel ophthalmic disease screening model using few-shot learning and style transfer data augmentation to improve generalization with limited data.

Area of Science:

  • Ophthalmology
  • Computer Vision
  • Machine Learning

Background:

  • Ophthalmic disease screening models often suffer from insufficient data, limiting their diagnostic capabilities.
  • Existing public datasets typically focus on single-disease screening, hindering comprehensive model development.
  • Developing robust screening models requires addressing data heterogeneity from various sources and imaging devices.

Purpose of the Study:

  • To construct a novel ophthalmic disease screening task and model adaptable to limited data scenarios.
  • To enhance the generalization ability of ophthalmic disease screening models.
  • To establish a practically valuable screening model for diverse eye conditions.

Main Methods:

  • A new dataset was created by merging data from diverse sources and cameras, enabling multi-disease fundus screening.

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  • Style transfer techniques were employed to simulate different fundus camera characteristics, augmenting training data.
  • A robust disease screening model was developed using few-shot learning on the combined dataset.

    • Main Results:

    • The proposed method significantly improved the generalization ability of the ophthalmic disease screening model.
    • Experiments demonstrated the effectiveness of few-shot learning and style transfer-based data augmentation.
    • The developed model showed superior performance compared to benchmark algorithms.

    Conclusions:

    • Integrating few-shot learning and style transfer data augmentation enhances ophthalmic disease screening model generalization.
    • The developed model possesses practical value for real-world ophthalmic disease screening applications.
    • This approach offers a viable solution for building effective screening tools with limited datasets.