UniDINO: Assay-independent feature extraction for fluorescence microscopy images.

ABSTRACT: High-content imaging (HCI) enables the characterization of cellular states through the extraction of quantitative features from fluorescence microscopy images. Despite the widespread availability of HCI data, the development of generalizable feature extraction models remains challenging due to the heterogeneity of microscopy images, as experiments often differ in channel count, cell type, and assay conditions. To address these challenges, we introduce uniDINO, a generalist feature extraction model capable of handling images with an arbitrary number of channels. We train uniDINO on a dataset of over 900,000 single-channel images from diverse experimental contexts and concatenate single-channel features to generate embeddings for multi-channel images. Our extensive validation across varied d

SUBMITTER: Morelli FM 

PROVIDER: S-EPMC11930362 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature