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

• URL: http://arxiv.org/abs/2409.16322v2
• Date: Wed, 28 May 2025 10:42:35 GMT
• Title: On the Within-class Variation Issue in Alzheimer's Disease Detection
• Authors: Jiawen Kang, Dongrui Han, Lingwei Meng, Jingyan Zhou, Jinchao Li, Xixin Wu, Helen Meng,
• Abstract summary: Alzheimer's Disease (AD) detection employs machine learning classification models to distinguish between individuals with AD and those without.<n>In this work, we found using a sample score estimator can generate sample-specific soft scores aligning with cognitive scores.<n>We propose two simple yet effective methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe)
• Score: 60.08015780474457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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