Multimodal deep learning for biomedical data fusion: a review

🤖 Plain-English Summary

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.

🔑 Key Findings

• Deep learning (DL)-based data fusion strategies are a popular approach for modeling these nonlinear relationships.
• 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.
• By doing so, we find that deep fusion strategies often outperform unimodal and shallow approaches.

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