Morphology exploration of pollen using deep learning latent space. (1st December 2022)

• Record Type: Journal Article
• Authors: Grant-Jacob, James A; Zervas, Michalis N; Mills, Ben
• Is Part Of: IOP SciNotes. Volume 3:Number 4(2022)
• Publication Date: 2022-12-01
• Subjects: deep learning -- latent space -- palynology -- evolution -- pollen; 500
• DOI: 10.1088/2633-1357/acadb9
• Languages: English
• ISSNs: 2633-1357

Abstract

Abstract: The structure of pollen has evolved depending on its local environment, competition, and ecology. As pollen grains are generally of size 10–100 microns with nanometre-scale substructure, scanning electron microscopy is an important microscopy technique for imaging and analysis. Here, we use style transfer deep learning to allow exploration of latent w-space of scanning electron microscope images of pollen grains and show the potential for using this technique to understand evolutionary pathways and characteristic structural traits of pollen grains.