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Annotation burden reduction in deep learning for...
Conference

Annotation burden reduction in deep learning for lensless imaging flow cytometry with a self-supervised pretext task

Abstract

A self-supervised pretext task is developed based on flow profile and motion extraction for cell detection in a lensless imaging flow cytometer. It reduces the annotation burden, automatically selects usable frames, and improves detection performance.

Authors

Hong T; Fang Q

Publisher

Optica Publishing Group

DOI

10.1364/boda.2023.jtu4b.12

Name of conference

Biophotonics Congress: Optics in the Life Sciences 2023 (OMA, NTM, BODA, OMP, BRAIN)