Dual Adaptive Disentangled Representation Learning With Multimodal Data for Disease Diagnosis

The use of imaging and genetic data for biomarker detection and disease diagnosis can deepen the understanding of disease pathogenesis and assist in clinical diagnosis. However, current methods face two major challenges: 1) the significant heterogeneity between multimodal data hampers modality fusion and 2) effectively exploring consistency and variability information from similar diseases for enhancing model performance is difficult. In this paper, we propose a novel unified framework, termed dual adaptive disentangled representation learning (DADRL), to simultaneously achieve disease-shared and disease-specific biomarker detection as well as disease diagnosis. Our DADRL comprises three components: 1) a biology information constraints-based modality fusion strategy is applied to adaptively explore inter- and intra-modal correlations, thereby effectively fusing multimodal data; 2) a unified framework that integrates modality fusion and disease diagnosis is proposed to mine disease-related information for simultaneously accomplishing disease-related biomarker detection and disease diagnosis; and 3) disentangled representation learning and several adaptive metric constraints are incorporated into the unified framework to adaptively separate disease-specific information from disease-shared feature representations for effectively identifying disease-shared and disease-specific biomarkers, thereby deepening the understanding of disease pathogenesis. Extensive experiments on multiple real datasets and simulated data demonstrate that our method significantly improves performance of biomarker detection and disease diagnosis.