Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images. (arXiv:2203.13875v2 [cond-mat.mtrl-sci] UPDATED)

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 …

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