Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey
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Authors
Wei, Ruoqi
Mahmood, Ausif
Issue Date
2020-12-31
Type
Article
Language
en_US
Keywords
Deep learning , Variational autoencoders , Data representation , Generative models , Unsupervised learning , Representation learning , Latent space , Biomedical informatics
Alternative Title
Abstract
Variational autoencoders (VAEs) are deep latent space generative models that have been immensely successful in multiple exciting applications in biomedical informatics such as molecular design, protein design, medical image classification and segmentation, integrated multi-omics data analyses, and large-scale biological sequence analyses, among others. The fundamental idea in VAEs is to learn the distribution of data in such a way that new meaningful data with more intra-class variations can be generated from the encoded distribution. The ability of VAEs to synthesize new data with more representation variance at state-of-art levels provides hope that the chronic scarcity of labeled data in the biomedical field can be resolved. Furthermore, VAEs have made nonlinear latent variable models tractable for modeling complex distributions. This has allowed for efficient extraction of relevant biomedical information from learned features for biological data sets, referred to as unsupervised feature representation learning. In this article, we review the various recent advancements in the development and application of VAEs for biomedical informatics. We discuss challenges and future opportunities for biomedical research with respect to VAEs.
Description
Citation
R. Wei and A. Mahmood, "Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey," in IEEE Access, vol. 9, pp. 4939-4956, 2021, doi: 10.1109/ACCESS.2020.3048309.
Publisher
IEEE