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Related Experiment Videos

Lesion Learning Network with Relation Aware Transformer for Diabetic Retinopathy Grading.

Hao Liang, Zhaoshui He, Zhijie Lin

    IEEE Journal of Biomedical and Health Informatics
    |April 10, 2026
    PubMed
    Summary
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    A new deep learning model, Lesion Learning Network (LLNet), precisely grades Diabetic Retinopathy (DR) by focusing on lesion features. This AI approach improves diagnostic efficiency and accuracy for ophthalmologists, aiding early detection of DR.

    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Diabetic Retinopathy (DR) is a primary cause of irreversible blindness.
    • Early DR screening is difficult, impacting timely diagnosis and treatment.
    • Current automated DR grading methods struggle with intra-class variations and small lesion detection.

    Purpose of the Study:

    • To develop a deep learning model for precise Diabetic Retinopathy grading.
    • To enhance the diagnostic efficiency and accuracy of ophthalmologists in DR detection.
    • To overcome challenges posed by intra-class variations and small lesions in DR grading.

    Main Methods:

    • A Lesion Learning Network (LLNet) incorporating a Relation Aware Transformer was proposed.
    • A Lesion Information Extractor (LIE) identified and extracted fine-grained lesion features.

    Related Experiment Videos

  • A Lesion Saliency Transformer (LST) enhanced perception of small lesions.
  • A Lesion Relation Aware Transformer (LRAT) modeled relationships between lesion conditions and severity grades.

    • Main Results:

    • LLNet achieved high accuracies on FGADR (82.7%), DDR (86.1%), and APTOS (87.2%) datasets.
    • The model demonstrated superior generalization, achieving 72.1% accuracy when trained on DDR and tested on EyePACS.
    • LLNet surpassed existing comparative methods in DR grading precision.

    Conclusions:

    • The proposed LLNet effectively addresses challenges in DR grading, including small lesions and intra-class variations.
    • LLNet offers a robust and accurate automated solution for DR grading, improving diagnostic efficiency.
    • The adaptive lesion-learning strategy enhances data utilization and model generalization for DR screening.