An Adaptive Tabular-guided Layerwise Alignment and Synthesis Network for Multimodal Neuroimaging and Clinical Phenotype Fusion
Brain disorders involve abnormalities spanning multiple biological and phenotypic scales that require the integration of multiple data sources, including neuroimaging and structured clinical data, to achieve accurate diagnosis. However, effectively integrating heterogeneous imaging modalities with tabular clinical data remains challenging due to fundamental differences in data structure, dimensionality, and semantics. Existing fusion strategies often restrict fusion to a single network layer, hindering the progressive integration of complementary features across multiple layers and limiting the sufficiency and flexibility of interactions between modalities. To address these limitations, we propose an Adaptive Tabular-guided Layerwise Alignment and Synthesis Network (ATLAS-Net) for effective multimodal neuroimaging and tabular data integration. Specifically, ATLAS-Net introduces an adaptive layerwise alignment (ALA) module that progressively aligns and fuses features from imaging branches through attention-driven interactions at multiple network layers, enabling continuous and sufficient cross-modality information exchange. In addition, we design a tabular-adaptive parameterization (TAP) module, which incorporates structured clinical variables as conditional priors to dynamically generate network parameters and modulate intermediate feature channels, thereby allowing tabular information to directly guide image feature extraction throughout the network hierarchy. Extensive experiments on 8 brain disorders demonstrate that ATLAS-Net consistently outperforms state-of-the-arts in classification performance, cross-dataset generalization, and robustness to realistic spatial noise perturbations. The code is available at https://github.com/liangchuang11/ATLAS-Net.git.