DyMix: Dynamic frequency Mixup scheduler based unsupervised domain adaptation for enhancing Alzheimer’s disease prediction

Le résumé fourni par la source

Recent advances in deep learning (DL) have substantially improved the accuracy of Alzheimer’s disease (AD) diagnosis from brain images, enabling earlier and more reliable clinical interventions. Nevertheless, most DL-based models often suffer from significant performance degradation when applied to unseen domains owing to variations in data distributions, a challenge commonly referred to as domain shift. To address this issue, we propose DyMix, a dynamic frequency Mixup scheduler for unsupervised domain adaptation (UDA). Built upon a Fourier transformation, DyMix dynamically adjusts the frequency components between source and target domains within selected regions, allowing the model to efficiently capture domain-relevant information. To further enhance robustness, DyMix incorporates intensity-invariant learning and self-adversarial regularization, encouraging the extraction of stable and domain-invariant feature representations. Such an adaptive framework enables robust cross-domain generalization by dynamically aligning domain-specific frequency characteristics while maintaining informative disease-relevant representations. Extensive experiments on two benchmark datasets ( i.e. , ADNI and AIBL) demonstrate that DyMix consistently outperforms state-of-the-art UDA methods for AD diagnosis. As a result, our method has achieved average performance gains of +6.04% in accuracy and +5.88% in AUC compared to the mean score of all baseline methods across multiple cross-domain scenarios. The code is available at: https://github.com/ku-milab/DyMix .

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