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

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.