针对阿尔茨海默病检测中的类内差异，提出新方法提升识别准确率。
On the Within-class Variation Issue in Alzheimer's Disease Detection
- 用样本级概率得分建模类内异质性与实例不平衡问题。
- 在ADReSS和CU-MARVEL数据集上显著提升检测性能。
- 适合语音辅助阿尔茨海默病早期筛查的研究者参考。
阿尔茨海默病（AD）检测通常采用机器学习分类模型区分患病与非患病个体。与常规分类任务不同，AD检测存在显著的类内变异，即相同诊断群体中的个体表现出不同程度的认知障碍。本文提出两个方面的类内变异：类内异质性与实例级不平衡。为在二元监督下建模此类变异，我们估计每个样本的AD类别概率作为样本得分，并提出两种方法：软目标蒸馏（SoTD）与实例级重平衡（InRe）。在ADReSS和CU-MARVEL语料库上的实验表明，所估得分与独立认知评估结果一致，且所提方法提升了AD检测性能。这些发现为基于语音的AD检测中类内变异建模提供了新思路。
原文摘要 · Abstract (English)
Alzheimer's Disease (AD) detection commonly employs machine learning classification models to distinguish between individuals with AD and those without. Different from conventional classification tasks, AD detection involves substantial within-class variation, as individuals sharing the same diagnosis may exhibit different degrees of cognitive impairment. We formulate two aspects of this issue: within-class heterogeneity and instance-level imbalance. To model such variation under binary supervision, we estimate sample-specific AD class probabilities as sample scores and develop two corresponding methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe). Experiments on the ADReSS and CU-MARVEL corpora show that the estimated scores align with independent cognitive assessments and that the proposed approaches improve AD detection performance. These findings provide insights for modeling within-class variation in speech-based AD detection.
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