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Annotation burden reduction in deep learning for...
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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

Publication Date

January 1, 2023

DOI

10.1364/NTM.2023.JTu4B.12

Conference proceedings

Novel Techniques in Microscopy NTM 2023
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