ArticleNeuroimage. Reports2026
An interpretable cross-attentive multi-modal MRI fusion framework for schizophrenia identification.
Article in Neuroimage. Reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Learning heritable multimodal brain representation via contrastive learning.Research square · 2026Article
- BrainGeneBot: a framework for variant prioritization and generative pretrained transformer-informed interpretation across polygenic risk score studies.Briefings in bioinformatics · 2025Article
- Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks.Medical image analysis · 2025Article
- Article
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Authors and funding
9 authors.
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Abstract
Functional MRI (fMRI) and structural MRI (sMRI) offer complementary insights into brain function and anatomy, but their integration for schizophrenia identification remains challenging due to modality heterogeneity. Many existing methods fall short of effective modeling of the interaction between two modalities. We propose CAMF, a Cross-Attentive Multi-modal Fusion framework that employs self-attention to capture intra-modal patterns and cross-attention to learn inter-modal relationships. In addition, we introduce the gradient-guided score-class activation map to enhance interpretability by highlighting salient features. Our approach significantly improves the accuracy in classifying schizophrenia, as demonstrated by the evaluation of multi-modal brain imaging datasets from four cohorts of schizophrenia studies. Furthermore, the model identifies functional networks and anatomical regions aligned with established biomarkers. CAMF provides an accurate and interpretable framework for multimodal brain imaging analysis, offering new insights into schizophrenia-related alterations.
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