Anomaly detection for high-content image-based phenotypic cell profiling.

ABSTRACT: High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell's physiological state. Classical representations of the phenotypic profile can not capture the full underlying complexity in cell organization, while recent weakly machine-learning based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and use it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated fo

SUBMITTER: Shpigler A 

PROVIDER: S-EPMC11185510 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature