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

Overview

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

Abstract

We propose ADGNET, a semi-supervised framework for Alzheimer’s disease (AD) diagnosis that jointly optimizes image reconstruction and classification through shared feature representations. The architecture integrates a residual backbone with attention modulation for dynamic feature selection, an encoder-decoder reconstruction branch for unsupervised representation learning, and a classification branch with focal loss to address class imbalance. This dual-task design enables effective feature learning from limited annotations. On two public MRI datasets—KACD (2D, 6,400 images) and ROAD (3D, 532 scans)—ADGNET achieves average performance improvements of 4.1% and 7.2% over state-of-the-art methods (ResNeXt WSL, SimCLR) across six metrics. Interpretability analysis using Grad-CAM and attention visualization confirms that the model focuses on clinically relevant neuroanatomical structures, particularly the hippocampus and temporal lobes, with strong correlation to established AD pathology (r = 0.67, p < 0.001). These results validate the model’s exceptional generalization capability and feature representation effectiveness across multi-modal medical imaging data, offering an efficient solution for few-shot medical image analysis.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/tourist55 — at Kaggle; found in the references

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 2, 28 September 2026

• Funding: added Zhejiang Shuren University; Natural Science Foundation of Zhejiang Province: Y1110023

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 7 MeSH terms, 5 references.

Cite

This paper

Yang, X. (2026). Reducing annotation burden in medical imaging with ADGNET: A semi-supervised deep learning strategy. PloS one, 21(5), e0348596. https://doi.org/10.1371/journal.pone.0348596

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