Enhancing Depression Detection via Question-wise Modality Fusion
Journal:
arXiv
Published Date:
Mar 26, 2025
Abstract
Depression is a highly prevalent and disabling condition that incurs
substantial personal and societal costs. Current depression diagnosis involves
determining the depression severity of a person through self-reported
questionnaires or interviews conducted by clinicians. This often leads to
delayed treatment and involves substantial human resources. Thus, several works
try to automate the process using multimodal data. However, they usually
overlook the following: i) The variable contribution of each modality for each
question in the questionnaire and ii) Using ordinal classification for the
task. This results in sub-optimal fusion and training methods. In this work, we
propose a novel Question-wise Modality Fusion (QuestMF) framework trained with
a novel Imbalanced Ordinal Log-Loss (ImbOLL) function to tackle these issues.
The performance of our framework is comparable to the current state-of-the-art
models on the E-DAIC dataset and enhances interpretability by predicting scores
for each question. This will help clinicians identify an individual's symptoms,
allowing them to customise their interventions accordingly. We also make the
code for the QuestMF framework publicly available.
