Multimodal deep learning for biomedical data fusion: a review

Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. Similarly, utilizing transfer learning might overcome sample size limitations of multimodal data sets.

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• 1 Deep learning (DL)-based data fusion strategies are a popular approach for modeling these nonlinear relationships.
• 2 Therefore, we review the current advanced of such methods and propose a detailed taxonomy that facilitates more informed choices of fusion strategies for biomedical applications, as well as research on novel methods.
• 3 By doing so, we find that deep fusion strategies often outperform unimodal and shallow approaches.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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