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

by   Gary B. Huang, et al.

We propose a method to facilitate exploration and analysis of new large data sets. In particular, we give an unsupervised deep learning approach to learning a latent representation that captures semantic similarity in the data set. The core idea is to use data augmentations that preserve semantic meaning to generate synthetic examples of elements whose feature representations should be close to one another. We demonstrate the utility of our method applied to nano-scale electron microscopy data, where even relatively small portions of animal brains can require terabytes of image data. Although supervised methods can be used to predict and identify known patterns of interest, the scale of the data makes it difficult to mine and analyze patterns that are not known a priori. We show the ability of our learned representation to enable query by example, so that if a scientist notices an interesting pattern in the data, they can be presented with other locations with matching patterns. We also demonstrate that clustering of data in the learned space correlates with biologically-meaningful distinctions. Finally, we introduce a visualization tool and software ecosystem to facilitate user-friendly interactive analysis and uncover interesting biological patterns. In short, our work opens possible new avenues in understanding of and discovery in large data sets, arising in domains such as EM analysis.


page 8

page 15

page 16

page 19


Interpretable Discovery in Large Image Data Sets

Automated detection of new, interesting, unusual, or anomalous images wi...

Unsupervised Representations of Pollen in Bright-Field Microscopy

We present the first unsupervised deep learning method for pollen analys...

Visualizing Image Content to Explain Novel Image Discovery

The initial analysis of any large data set can be divided into two phase...

SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation

Region-based methods have proven necessary for improving segmentation ac...

Visual Pattern-Driven Exploration of Big Data

Pattern extraction algorithms are enabling insights into the ever-growin...

Unsupervised learning with contrastive latent variable models

In unsupervised learning, dimensionality reduction is an important tool ...

FDive: Learning Relevance Models using Pattern-based Similarity Measures

The detection of interesting patterns in large high-dimensional datasets...

Please sign up or login with your details

Forgot password? Click here to reset