Mimicry embedding for advanced neuralnetwork training of 3D biomedical micrographs

Yakimovich, A.; Huttunen, M.; Samolej, J.; Clough, B.; Yoshida, N.; Mostowy, S.; Frickel, E.; Mercer, J.

doi:10.1101/820076

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bioRxiv · 10.1101/820076

Mimicry embedding for advanced neuralnetwork training of 3D biomedical micrographs

Yakimovich, A.·Huttunen, M.·Samolej, J.·Clough, B.·Yoshida, N.·Mostowy, S.·Frickel, E.·Mercer, J.

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Abstract

The use of deep neural networks (DNNs) for analysis of complex biomedical images shows great promise but is hampered by a lack of large verified datasets for rapid network evolution. Here we present a novel \"mimicry embedding\" strategy for rapid application of neural network architecture-based analysis of biomedical imaging datasets. Embedding of a novel biological dataset, such that it mimics a verified dataset, enables efficient deep learning and seamless architecture switching. We apply this strategy across various microbiological phenotypes; from super-resolved viruses to in vivo parasitic infections. We demonstrate that mimicry embedding enables efficient and accurate analysis of three-dimensional microscopy datasets. The results suggest that transfer learning from pre-trained network data may be a powerful general strategy for analysis of heterogeneous biomedical imaging datasets.

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Yakimovich, A., Huttunen, M., Samolej, J., Clough, B., Yoshida, N., Mostowy, S., Frickel, E., Mercer, J.. 2019-10-28. Mimicry embedding for advanced neuralnetwork training of 3D biomedical micrographs. https://doi.org/10.1101/820076

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