Artifact detection in fluorescence microscopy using convolutional autoencoder.

ABSTRACT: To ensure analytical accuracy in fluorescence microscopy image analysis, robust artifact detection is essential. For large datasets or time-sensitive analyses, automation is advisable, as it not only reduces time and costs but also eliminates human bias and enhances reproducibility. Although artificial intelligence is commonly employed for artifact detection, it is typically limited to recognizing artifact types that have been previously learned, often necessitating large training datasets. This study proposes an approach for an automated detection of previously unseen artifacts without the need for a training set of artifact-laden images. Multiple datasets were assembled using images generated by our surface-based intensity distribution analysis (sFIDA) technology during different experim

SUBMITTER: Rehn F 

PROVIDER: S-EPMC12432141 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature