Alzheimer's Disease (AD) detection employs machine learning classification models to distinguish between individuals with AD and those without. Different from conventional classification tasks, we identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. Therefore, simplistic binary AD classification may overlook two crucial aspects: within-class heterogeneity and instance-level imbalance. In this work, we found using a sample score estimator can generate sample-specific soft scores aligning with cognitive scores. We subsequently propose two simple yet effective methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively. Based on the ADReSS and CU-MARVEL corpora, we demonstrated and analyzed the advantages of the proposed approaches in detection performance. These findings provide insights for developing robust and reliable AD detection models.
@article{arxiv.2409.16322,
title = {On the Within-class Variation Issue in Alzheimer's Disease Detection},
author = {Jiawen Kang and Dongrui Han and Lingwei Meng and Jingyan Zhou and Jinchao Li and Xixin Wu and Helen Meng},
journal= {arXiv preprint arXiv:2409.16322},
year = {2025}
}
Comments
Accepted for publication in Proc. of Interspeech 2025 conference. Note: this is an extended version of the conference paper, with an additional section included