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Reducing annotation burden in medical imaging with ADGNET: A semi-supervised deep learning strategy.

¹Department of Information Science and Technology, Zhejiang Shuren University, Hangzhou, Zhejiang, P. R.China.

|May 4, 2026

Summary

ADGNET, a novel semi-supervised framework, enhances Alzheimer's disease (AD) diagnosis by jointly learning image reconstruction and classification. This approach improves accuracy with limited data, focusing on key brain regions for reliable diagnosis.

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Area of Science:

Neuroimaging
Artificial Intelligence
Medical Diagnostics

Background:

Alzheimer's disease (AD) diagnosis relies heavily on medical imaging, but limited annotated data poses a challenge for deep learning models.
Existing methods often struggle with class imbalance and effective feature extraction from sparse datasets.

Purpose of the Study:

To introduce ADGNET, a semi-supervised framework for improved Alzheimer's disease diagnosis using magnetic resonance imaging (MRI).
To leverage a dual-task learning approach for joint image reconstruction and classification, enhancing feature representation with limited annotations.

Main Methods:

ADGNET integrates a residual backbone with attention, an encoder-decoder for unsupervised learning, and a classification branch with focal loss.
The framework optimizes shared feature representations for both image reconstruction and AD classification tasks.

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Utilized two public MRI datasets: KACD (2D) and ROAD (3D) for model validation.

Main Results:

ADGNET demonstrated significant performance improvements over state-of-the-art methods (ResNeXt WSL, SimCLR) by 4.1% (KACD) and 7.2% (ROAD) across six metrics.
Interpretability analyses (Grad-CAM, attention visualization) confirmed the model's focus on clinically relevant regions like the hippocampus and temporal lobes.
The model showed strong correlation (r=0.67, p<0.001) between its learned features and established Alzheimer's pathology.

Conclusions:

ADGNET offers an efficient and effective solution for few-shot medical image analysis in Alzheimer's disease diagnosis.
The framework exhibits strong generalization capabilities across multi-modal medical imaging data.
The joint optimization of reconstruction and classification enhances feature learning and diagnostic accuracy.