Author

Li-Fang He

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#machine learning May 2026

Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis

Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework for integrative brain network analysis that generalizes Symmetric Nonnegative Matrix Tri-Factorization from unsupervised single-graph clustering to supervised prediction over populations of multimodal graphs, is presented.

Amjad Seyedi, Li-Fang He, Song-Lin Zhao et al. · arXiv.org · 0 citations

Open access Aug 2026

Interpretable multimodal learning for integrating neuroimaging and genetic data in Alzheimer’s disease

R-GenIMA, an interpretable multimodal large language model that pairs a region-of-interest vision transformer with genetic prompting to jointly analyze structural MRI and single nucleotide polymorphisms, shows that interpretable multimodal AI can integrate imaging and genetics to reveal disease mechanisms.

Kun Zhao, Si-Yuan Dai, Ying-Ying Zhang et al. · Frontiers in Radiology · 0 citations