Self-supervised deep learning for highly efficient spatial immunophenotyping.

ABSTRACT:

Background

Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets.

Methods

This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry

SUBMITTER: Zhang H 

PROVIDER: S-EPMC10493897 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature