An energy- and data-efficient foundation model for high-content phenotypic screening
Benjamin Midtvedt, Jesús D. Pineda Castro, Henrik Klein Moberg, Jim Denholm, Mattias Goksör, Christos Matsoukas, Bjarki Johannesson, Giovanni Volpe
Date: 24 August 2026
Time: 5:30 PM – 7:30 PM PDT
Place: Conv. Ctr. Exhibit Hall B1
High-content screening (HCS) enables large-scale characterization of cellular responses to chemical and genetic perturbations, yet current deep learning approaches remain limited by label scarcity, batch effects, and high computational cost. We present an energy- and data-efficient foundation model for high-content phenotypic screening that unifies these challenges within a single architecture. Trained on the full JUMP Cell Painting dataset, the model combines active learning with contrastive supervision to focus representation learning on biologically informative variation while reducing sensitivity to technical artifacts. By supporting multimodal integration of molecular metadata alongside fluorescence morphology, the model learns generalizable cellular representations that transfer across experimental conditions and downstream phenotypic tasks.