Self-supervision advances morphological profiling by unlocking powerful image representations.

Cell Painting is an image-based assay that offers valuable insights into drug mechanisms of action and off-target effects. However, traditional feature extraction tools such as CellProfiler are computationally intensive and require frequent parameter adjustments. Inspired by recent advances in AI, we trained self-supervised learning (SSL) models DINO, MAE, and SimCLR on a subset of the JUMP Cell Painting dataset to obtain powerful representations for Cell Painting images.