PT  - JOURNAL ARTICLE
AU  - Artur Yakimovich
AU  - Moona Huttunen
AU  - Jerzy Samolej
AU  - Barbara Clough
AU  - Nagisa Yoshida
AU  - Serge Mostowy
AU  - Eva Frickel
AU  - Jason Mercer
TI  - Mimicry embedding for advanced neural network training of 3D biomedical micrographs
AID  - 10.1101/820076
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 820076
4099  - http://biorxiv.org/content/early/2019/10/29/820076.short
4100  - http://biorxiv.org/content/early/2019/10/29/820076.full
AB  - 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.