CDA: A Contrastive Data Augmentation Method for Alzheimer’s Disease Detection

Junwen Duan, Fangyuan Wei, Jin Liu, Hongdong Li, Tianming Liu, Jianxin Wang

Abstract
Alzheimer’s Disease (AD) is a neurodegenerative disorder that significantly impacts a patient’s ability to communicate and organize language. Traditional methods for detecting AD, such as physical screening or neurological testing, can be challenging and time-consuming. Recent research has explored the use of deep learning techniques to distinguish AD patients from non-AD patients by analysing the spontaneous speech. These models, however, are limited by the availability of data. To address this, we propose a novel contrastive data augmentation method, which simulates the cognitive impairment of a patient by randomly deleting a proportion of text from the transcript to create negative samples. The corrupted samples are expected to be in worse conditions than the original by a margin. Experimental results on the benchmark ADReSS Challenge dataset demonstrate that our model achieves the best performance among language-based models.
Anthology ID:
2023.findings-acl.114
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1819–1826
Language:
URL:
https://aclanthology.org/2023.findings-acl.114
DOI:
10.18653/v1/2023.findings-acl.114
Bibkey:
Cite (ACL):
Junwen Duan, Fangyuan Wei, Jin Liu, Hongdong Li, Tianming Liu, and Jianxin Wang. 2023. CDA: A Contrastive Data Augmentation Method for Alzheimer’s Disease Detection. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1819–1826, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
CDA: A Contrastive Data Augmentation Method for Alzheimer’s Disease Detection (Duan et al., Findings 2023)
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https://preview.aclanthology.org/nschneid-patch-5/2023.findings-acl.114.pdf
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