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arXiv · 2409.16322

On the Within-class Variation Issue in Alzheimer's Disease Detection

Jiawen Kang·Dongrui Han·Lingwei Meng·Jingyan Zhou·Jinchao Li·Xixin Wu·Helen Meng
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Abstract

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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Jiawen Kang, Dongrui Han, Lingwei Meng, Jingyan Zhou, Jinchao Li, Xixin Wu, Helen Meng. 2024-09-22. On the Within-class Variation Issue in Alzheimer's Disease Detection. https://doi.org/10.21437/interspeech.2025-2751

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