DyMix: Dynamic Frequency Mixup Scheduler based Unsupervised Domain Adaptation for Enhancing Alzheimer's Disease Identification

TL;DR

This paper introduces DyMix, a novel adaptive frequency mixup scheduler for unsupervised domain adaptation in Alzheimer's disease diagnosis, improving model robustness and accuracy across unseen data domains.

Contribution

The paper proposes DyMix, a dynamic frequency mixup method that adjusts mixing regions adaptively, enhancing domain generalization in deep learning models for AD diagnosis.

Findings

DyMix outperforms state-of-the-art methods on benchmark datasets.

DyMix improves model robustness against domain shifts.

Experimental results show significant accuracy gains in AD diagnosis.

Abstract

Advances in deep learning (DL)-based models for brain image analysis have significantly enhanced the accuracy of Alzheimer's disease (AD) diagnosis, allowing for more timely interventions. Despite these advancements, most current DL models suffer from performance degradation when inferring on unseen domain data owing to the variations in data distributions, a phenomenon known as domain shift. To address this challenge, we propose a novel approach called the dynamic frequency mixup scheduler (DyMix) for unsupervised domain adaptation. Contrary to the conventional mixup technique, which involves simple linear interpolations between predefined data points from the frequency space, our proposed DyMix dynamically adjusts the magnitude of the frequency regions being mixed from the source and target domains. Such an adaptive strategy optimizes the model's capacity to deal with domain variability, thereby enhancing its generalizability across the target domain. In addition, we incorporate additional strategies to further enforce the model's robustness against domain shifts, including leveraging amplitude-phase recombination to ensure resilience to intensity variations and applying self-adversarial learning to derive domain-invariant feature representations. Experimental results on two benchmark datasets quantitatively and qualitatively validated the effectiveness of our DyMix in that we demonstrated its outstanding performance in AD diagnosis compared to state-of-the-art methods.