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OrdPrune-KD: An Ordinal-Consistency-Based Model Compression Framework for Diabetic Retinopathy Grading.

Yuzhe Yan¹, Siqi Liang¹, Yifan Xia¹

¹School of Airspace Science and Engineering, Shandong University, Weihai 264209, China.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

This summary is machine-generated.

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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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OrdPrune-KD efficiently compresses diabetic retinopathy (DR) grading models using ordinal priors. This framework balances model size and accuracy, offering a deployable solution for lightweight DR grading systems.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Diabetic retinopathy (DR) grading requires accurate and efficient models.
Existing model compression techniques may not fully leverage ordinal relationships in DR grading.
Deploying lightweight yet high-performing DR grading systems is crucial for clinical applications.

Purpose of the Study:

To propose OrdPrune-KD, an ordinal-consistency-driven model compression framework for diabetic retinopathy grading.
To integrate grade-aware structured pruning and Earth Mover's Distance (EMD)-based knowledge distillation.
To incorporate ordinal priors into both model compression and knowledge transfer stages.

Main Methods:

Developed OrdPrune-KD, a novel framework combining structured pruning and knowledge distillation.

Keywords:

Earth Mover’s Distance (EMD)clinical decision support deep learning diabetic retinopathy grading knowledge distillation lightweight neural networks medical image analysis model compression ordinal learning structured pruning

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Incorporated ordinal priors into the model compression and knowledge transfer processes.

Utilized Earth Mover's Distance (EMD) for knowledge distillation.

Main Results:

The proposed framework achieved a favorable balance between model compactness and predictive performance.
A 77% parameter reduction was achieved with the student model showing competitive performance (QWK) and strong high-risk sensitivity.
Performance gains were attributed to the ordinal-aware design, not output formulation differences.

Conclusions:

OrdPrune-KD offers an effective and deployable solution for lightweight DR grading systems.
The ordinal-aware design enhances both compression and predictive performance.
This framework addresses the need for efficient and accurate DR grading tools.