AuD-Former: A Hierarchical Transformer Network for Multimodal Audio-Based Disease Prediction

• URL: http://arxiv.org/abs/2410.09289v1
• Date: Fri, 11 Oct 2024 22:37:52 GMT
• Title: AuD-Former: A Hierarchical Transformer Network for Multimodal Audio-Based Disease Prediction
• Authors: Jinjin Cai, Ruiqi Wang, Dezhong Zhao, Ziqin Yuan, Victoria McKenna, Aaron Friedman, Rachel Foot, Susan Storey, Ryan Boente, Sudip Vhaduri, Byung-Cheol Min,
• Abstract summary: Multimodal fusion, which integrates features from various domains within or across bio-acoustic modalities, has proven effective in enhancing diagnostic performance. Most existing methods in the field employ unilateral fusion strategies that focus solely on either intra-modal or inter-modal fusion. We propose AuD-Former, a hierarchical transformer network designed for general multimodal audio-based disease prediction.
• Score: 6.175036031779841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-based disease prediction is emerging as a promising supplement to traditional medical diagnosis methods, facilitating early, convenient, and non-invasive disease detection and prevention. Multimodal fusion, which integrates features from various domains within or across bio-acoustic modalities, has proven effective in enhancing diagnostic performance. However, most existing methods in the field employ unilateral fusion strategies that focus solely on either intra-modal or inter-modal fusion. This approach limits the full exploitation of the complementary nature of diverse acoustic feature domains and bio-acoustic modalities. Additionally, the inadequate and isolated exploration of latent dependencies within modality-specific and modality-shared spaces curtails their capacity to manage the inherent heterogeneity in multimodal data. To fill these gaps, we propose AuD-Former, a hierarchical transformer network designed for general multimodal audio-based disease prediction. Specifically, we seamlessly integrate intra-modal and inter-modal fusion in a hierarchical manner and proficiently encode the necessary intra-modal and inter-modal complementary correlations, respectively. Comprehensive experiments demonstrate that AuD-Former achieves state-of-the-art performance in predicting three diseases: COVID-19, Parkinson's disease, and pathological dysarthria, showcasing its promising potential in a broad context of audio-based disease prediction tasks. Additionally, extensive ablation studies and qualitative analyses highlight the significant benefits of each main component within our model.

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