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Multi-Scale Interactive Network With Artery/Vein Discriminator for Retinal Vessel Classification.

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    This study introduces a novel multi-scale interactive network with an A/V discriminator to accurately classify retinal arteries and veins, improving cardiovascular and eye disease diagnosis. The method effectively reduces arteriovenous confusion and handles noisy labels, achieving high accuracy on multiple datasets.

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    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Computer Vision

    Background:

    • Automatic classification of retinal arteries and veins is crucial for diagnosing cardiovascular and eye diseases.
    • Existing methods struggle with anatomical variations and inconsistent annotations, leading to vessel discontinuity and arteriovenous confusion.
    • Accurate A/V classification remains a significant challenge in retinal image analysis.

    Purpose of the Study:

    • To propose a multi-scale interactive network with an A/V discriminator for enhanced retinal artery and vein recognition.
    • To address challenges of arteriovenous confusion and noisy labels in A/V classification.
    • To improve the accuracy and robustness of automated A/V classification in fundus images.

    Main Methods:

    • Developed a multi-scale interaction (MI) module in the encoder for cross-space feature integration.
    • Designed an A/V discriminator (AVD) utilizing independent and shared information between arteries and veins, combined with topology loss.
    • Implemented a sample re-weighting (SW) strategy to mitigate the impact of labeling errors.

    Main Results:

    • Achieved high accuracy rates of 97.47% (AV-DRIVE), 96.91% (HRF), 97.79% (LES-AV), and 98.18% on a private dataset.
    • Demonstrated competitive performance compared to state-of-the-art methods for artery/vein classification.
    • Successfully reduced arteriovenous confusion and alleviated disturbances from noisy labels.

    Conclusions:

    • The proposed multi-scale interactive network with A/V discriminator offers a robust solution for retinal artery and vein classification.
    • The method effectively handles anatomical variations and labeling inconsistencies, leading to improved diagnostic assistance.
    • Public release of 100 annotated fundus images aims to foster further research in the field.