CellRep: A Multichannel Image Representation Learning Model

Reliable feature extraction from multichannel microscopy images is crucial for biological discovery, but existing models typically require fixed channel architectures or artificial RGB compositing. We introduce CellRep, a channel-invariant representation learning model that generates consistent feature representations across varying experimental conditions. By employing content-aware patch embedding and channel-mixing transformer encoding, CellRep learns to identify and represent biological structures independent of channel position or type. Our evaluations demonstrate CellRep’s strong performance as a microscopy image featurizer for perturbation prediction, particularly when generalizing to novel cell types, imaging techniques, and channel configurations not seen during training.