  In the field of materials science, microscopy is the first and often only
accessible method for structural characterization. There is a growing interest
in the development of machine learning methods that can automate the analysis
and interpretation of microscopy images. Typically training of machine learning
models requires large numbers of images with associated structural labels,
however, manual labeling of images requires domain knowledge and is prone to
human error and subjectivity. To overcome these limitations, we present a
semi-supervised transfer learning approach that uses a small number of labeled
microscopy images for training and performs as effectively as methods trained
on significantly larger image datasets. Specifically, we train an image encoder
with unlabeled images using self-supervised learning methods and use that
encoder for transfer learning of different downstream image tasks
(classification and segmentation) with a minimal number of labeled images for
training. We test the transfer learning ability of two self-supervised learning
methods: SimCLR and Barlow-Twins on transmission electron microscopy (TEM)
images. We demonstrate in detail how this machine learning workflow applied to
TEM images of protein nanowires enables automated classification of nanowire
morphologies (e.g., single nanowires, nanowire bundles, phase separated) as
well as segmentation tasks that can serve as groundwork for quantification of
nanowire domain sizes and shape analysis. We also extend the application of the
machine learning workflow to classification of nanoparticle morphologies and
identification of different type of viruses from TEM images.
