Work

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

Year: 2022

Type: article

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.

Source: Digital Discovery

DOI: 10.1039/d2dd00066k

Authors: Shizhao Lu, Brian Montz, Todd Emrick, Arthi Jayaraman

Institutions: University of Delaware, University of Massachusetts Amherst

Language: English

Topic: Advanced Electron Microscopy Techniques and Applications

Subfield: Structural Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: diamond

Funder: National Science Foundation

Awards: DMREF/Collaborative Research: Conductive Protein Nanowires as Next Generation Polymer Nanocomposite Fillers, DMREF/Collaborative Research: Conductive Protein Nanowires as Next Generation Polymer Nanocomposite Fillers

OpenAlex ID: W4296502740

FWCI: 3.114

Cites: 53

Cited by: 31

Related to: 10