학술논문
Uncertainty-Aware Label Contrastive Distribution Learning for Automatic Depression Detection
Document Type
Periodical
Source
IEEE Transactions on Computational Social Systems IEEE Trans. Comput. Soc. Syst. Computational Social Systems, IEEE Transactions on. 11(2):2979-2989 Apr, 2024
Subject
Language
ISSN
2329-924X
2373-7476
2373-7476
Abstract
Depression is one of the most common mental illnesses, affecting people’s quality of life and posing a risk to their health. Low-cost and objective automatic depression detection (ADD) is becoming increasingly critical. However, existing ADD methods usually treat depression detection as a regression problem for predicting patient health questionnaire-8 (PHQ-8) scores, ignoring the scores’ ambiguity caused by multiple questionnaire issues. To effectively leverage the score labels, we propose an uncertainty-aware label contrastive and distribution learning (ULCDL) method to estimate PHQ-8 scores, thus detecting depression automatically. ULCDL first simulates the ambiguity within PHQ-8 scores by converting single-valued scores into discrete label distributions. Afterward, it learns to predict the PHQ-8 score distribution by minimizing the Kullback–Leibler (KL) divergence between the score distribution and the discrete label distribution. Finally, the predicted PHQ-8 score distribution outperforms the PHQ-8 score in ADD. Moreover, label-based contrastive learning (LBCL) is introduced to facilitate the model for learning common features related to depression in multimodal data. A multibranch fusion module is proposed to align and fuse multimodal data for better exploring the uncertainty of PHQ-8 labels. The proposed method is evaluated on the publicly available DAIC-WOZ dataset. Experiment results show that ULCDL outperforms regression-based depression detection methods and achieves state-of-the-art performance. The code will be released after acceptance.