Generalization Across Experimental Parameters in Machine Learning
  Analysis of High Resolution Transmission Electron Microscopy Datasets
Katherine Sytwu Luis Rangel DaCosta Mary C. Scott
Neural networks are promising tools for high-throughput and accurate
transmission electron microscopy (TEM) analysis of nanomaterials, but are known
to generalize poorly on data that is "out-of-distribution" from their training
data. Given the limited set of image features typically seen in high-resolution
TEM imaging, it is unclear which images are considered out-of-distribution from
others. Here, we investigate how the choice of metadata features in the
training dataset influences neural network performance, focusing on the example
task of nanoparticle segmentation. We train and validate neural networks across
curated, experimentally-collected high-resolution TEM image datasets of
nanoparticles under controlled imaging and material parameters, including
magnification, dosage, nanoparticle diameter, and nanoparticle material.
Overall, we find that our neural networks are not robust across microscope
parameters, but do generalize across certain sample parameters. Additionally,
data preprocessing heavily influences the generalizability of neural networks
trained on nominally similar datasets. Our results highlight the need to
understand how dataset features affect deployment of data-driven algorithms.