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Diabetic Retinopathy

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Domain generalization for diabetic retinopathy grading with phase augmentation framework.

Qianchen Zhang¹, Feng Liu²

¹College of Information Engineering, Shanghai Maritime University, HaiGang Avenue 1550#, Pudong District, 201306, Shanghai, China.

Medical & Biological Engineering & Computing

|November 6, 2025

Summary

This study introduces a Fourier-based framework to improve automatic diabetic retinopathy grading across different hospitals. The method enhances generalization performance, crucial for preventing vision loss from this common diabetic complication.

Keywords:

Data augmentation Diabetic retinopathy Domain generalization Image classification

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic retinopathy (DR) is a leading cause of blindness, necessitating automated grading.
Current deep learning models for DR grading struggle with generalization due to variations in clinical imaging data.

Purpose of the Study:

To address the domain generalization (DG) challenge in automated diabetic retinopathy grading.
To propose a novel Fourier-based framework to enhance the robustness and generalization of DR grading models.

Main Methods:

Developed a Fourier-based domain generalization framework incorporating Fourier spectrum enhancement using phase information.
Implemented collaborative teacher-student knowledge distillation to transfer high-level semantic features.
Utilized a feature fusion module to improve intra-class and inter-class feature differentiation.

Main Results:

The proposed framework demonstrated superior generalization performance on six diverse DR datasets compared to existing methods.
Fourier spectrum enhancement significantly improved cross-domain robustness by preserving high-frequency features.
Knowledge distillation and feature fusion further enhanced classification accuracy and generalization.

Conclusions:

The novel Fourier-based framework effectively tackles the domain generalization problem in diabetic retinopathy grading.
Fourier phase information and high-level semantic features are critical for improving model generalization in medical image analysis.
This approach offers a promising solution for reliable automated DR grading in diverse clinical settings.