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Learning for retinal image quality assessment with label regularization.

Tianjiao Guo1, Ziyun Liang2, Yun Gu2

  • 1Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China; School of Biomedical Engineering, Shanghai Jiao Tong Univeristy, Shanghai, China.

Computer Methods and Programs in Biomedicine
|November 24, 2022
PubMed
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This summary is machine-generated.

This study introduces a novel deep learning framework for accurate retinal image quality assessment. The method addresses label ambiguity and regression properties, outperforming existing approaches for computer-aided diagnosis.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Accurate fundus image quality assessment is vital for computer-aided diagnosis.
  • Current methods face challenges due to subjective grading and the regression nature of quality assessment.
  • Extracting discriminative features and addressing label ambiguity are key.

Purpose of the Study:

  • To develop a robust framework for accurate and reasonable fundus image quality assessment.
  • To overcome the limitations of subjective labels and the regression properties of grading.
  • To improve the performance of computer-aided diagnosis systems.

Main Methods:

  • A dual-path convolutional neural network with attention blocks was designed to extract discriminative features.
  • Label smoothing and cost-sensitive regularization were employed to handle label ambiguity and regression problems.
Keywords:
Convolutional neural network (CNN)Cost-sensitiveLabel smoothingQualityRetinal image

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  • A large dataset of annotated retinal images was utilized for training and validation.

    • Main Results:

    • The proposed framework was evaluated on the largest retinal image quality assessment dataset (28,792 images).
    • Achieved high performance metrics: 0.8868 precision, 0.8786 recall, 0.8820 F1, and 0.9138 Kappa score.
    • Outperformed state-of-the-art methods in retinal image quality assessment.

    Conclusions:

    • The developed deep learning methods significantly benefit retinal image quality assessment.
    • The framework shows potential for application in other image grading tasks.
    • Accurate image quality assessment is crucial for reliable computer-aided diagnosis.