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A dual-branch deep learning framework with Mask-Guided Attention for thyroid nodule classification in ultrasound images
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DOI：10.3389/fmed.2026.1694174.png)
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Abstract
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Thyroid nodules are common; and accurate classification into benign or malignant types is essential for effective clinical management. Although high-resolution ultrasound is the primary diagnostic tool; its accuracy is limited by operator dependency. Recent advances in deep learning have shown promise for automated and objective assessment; but many existing methods lack focus on lesion-specific regions; compromising model robustness. To overcome these limitations; we propose a novel dual-branch deep learning framework that combines lesion segmentation and classification. A key feature of this framework is a nodule mask-guided feature enhancement module; which leverages probability masks from the segmentation branch to guide the classification branch toward diagnostically relevant regions while suppressing irrelevant information. Evaluated on ultrasound datasets from three medical centers; our approach demonstrates superior classification accuracy compared to baseline methods; highlighting its potential as a reliable computer-aided diagnosis tool for thyroid nodules.
Keywords:
deep learning
classification
attention mechanism
thyroid nodules
lesion segmentation
ultrasound imaging
computer-aided diagnosis
weak supervision
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