Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images-Reference-Cited by-同舟云学术

Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images

Published:2022 Issue:6 Volume:1 Page:816-833
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Lu Shizhao¹^ORCID,Montz Brian²,Emrick Todd²^ORCID,Jayaraman Arthi¹³^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy St., Newark 19716, Delaware, USA

2. Department of Polymer Science and Engineering, University of Massachusetts Amherst, Amherst 01003, Massachusetts, USA

3. Department of Materials Science and Engineering, University of Delaware, 201 DuPont Hall, Newark 19716, Delaware, USA

Abstract

Semi-supervised transfer learning workflow facilitates rapid, automated nanomaterial morphology classification for small image datasets. Self-supervised training enables label-free pretraining that minimizes drawbacks of manual labeling.

Funder

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2022/DD/D2DD00066K

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