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Label-Aware Dual Graph Neural Networks for Multi-Label Fundus Image Classification.

Yanbei Liu, Xinwen Peng, Xin Wei

IEEE Journal of Biomedical and Health Informatics

|September 10, 2024

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This study introduces a new dual graph neural network for multi-label fundus image classification. The model improves diagnosis by considering patient similarities and pathology correlations, outperforming existing methods.

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Fundus diseases require early diagnosis for effective treatment.
Current deep learning models often overlook subject similarities and pathology correlations.
Accurate multi-label classification of fundus images remains a challenge.

Purpose of the Study:

To propose a novel label-aware dual graph neural network for multi-label fundus image classification.
To address limitations of existing methods by incorporating inter-subject and inter-pathology relationships.
To enhance the accuracy and adaptivity of fundus disease diagnosis.

Main Methods:

Developed a dual graph neural network with population-based and pathology-based modules.
Constructed a population graph integrating image and non-image data for patient representation.

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Created a pathology graph representing label co-occurrence for classifier score generation.

Main Results:

The proposed model effectively learns patient representations and pathology correlations.
Achieved superior performance compared to state-of-the-art multi-label fundus image classification methods.
Demonstrated adaptive recalibration of multi-label outputs.

Conclusions:

The label-aware dual graph neural network offers a significant advancement in multi-label fundus image classification.
Integrating population and pathology graphs enhances diagnostic accuracy.
This approach provides a more comprehensive analysis of fundus images for disease identification.