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We propose a method to facilitate exploration and analysis of new large datasets. In particular, we give an unsupervised deep learning approach to learninga latent representation that captures semantic similarity in the data set. Thecore idea is to use data augmentations that preserve semantic meaning togenerate synthetic examples of elements whose feature representations should beclose to one another.  We demonstrate the utility of our method applied to nano-scale electronmicroscopy data, where even relatively small portions of animal brains canrequire terabytes of image data. Although supervised methods can be used topredict and identify known patterns of interest, the scale of the data makes itdifficult to mine and analyze patterns that are not known a priori. We show theability of our learned representation to enable query by example, so that if ascientist notices an interesting pattern in the data, they can be presentedwith other locations with matching patterns. We also demonstrate thatclustering of data in the learned space correlates with biologically-meaningfuldistinctions. Finally, we introduce a visualization tool and software ecosystemto facilitate user-friendly interactive analysis and uncover interestingbiological patterns. In short, our work opens possible new avenues inunderstanding of and discovery in large data sets, arising in domains such asEM analysis.

Latent Feature Representation via Unsupervised Learning for Pattern Discovery in Massive Electron Microscopy Image Volumes