Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models to distinguish between individuals with AD and those without. Unlike conventional classification tasks, we identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. Given that many AD detection tasks lack fine-grained labels, simplistic binary classification may overlook two crucial aspects: within-class differences and instance-level imbalance. The former compels the model to map AD samples with varying degrees of impairment to a single diagnostic label, disregarding certain changes in cognitive function. While the latter biases the model towards overrepresented severity levels. This work presents early efforts to address these challenges. We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively. Experiments on the ADReSS and ADReSSo datasets demonstrate that the proposed methods significantly improve detection accuracy. Further analysis reveals that SoTD effectively harnesses the strengths of multiple component models, while InRe substantially alleviates model over-fitting. These findings provide insights for developing more robust and reliable AD detection models.

翻译：阿尔茨海默病检测已成为一个前景广阔的研究领域，该领域采用机器学习分类模型来区分患病个体与健康个体。与传统分类任务不同，我们发现类内变异是AD检测中的关键挑战：AD患者表现出不同程度的认知功能障碍。鉴于许多AD检测任务缺乏细粒度标签，简单的二分类可能忽略两个关键方面：类内差异与实例级不平衡。前者迫使模型将具有不同损伤程度的AD样本映射到单一诊断标签，从而忽视了认知功能的某些变化；而后者则使模型偏向于过度代表的严重程度级别。本研究针对这些挑战提出了初步解决方案。我们分别针对这两个问题提出了两种新方法：软目标蒸馏与实例级重平衡。在ADReSS和ADReSSo数据集上的实验表明，所提方法显著提升了检测准确率。进一步分析揭示：SoTD能有效整合多个组件模型的优势，而InRe则能大幅缓解模型过拟合现象。这些发现为开发更稳健可靠的AD检测模型提供了重要参考。