Responder-Weighted Flow Matching for Cell Morphology

Generative models of cell morphology, such as CellFlux, learn to turn images of control cells into images of treated cells. They are trained on every imaged cell of a perturbation, although often only some of these cells respond. We show that a perfectly trained flow then reproduces this mixture, so its predicted change of any feature equals the responder effect times the fraction of responding cells. No single classifier-free guidance strength undoes this, because the required strength grows as one over that fraction. We therefore estimate for every treated cell the probability that it responded. A classifier compares the cell with control cells of the same plate and never sees the cell's own well during training. We then weight each cell's flow-matching loss by this probability. Estimated responder fractions in public datasets are often significantly smaller than one, especially CRISPR knockouts. In a benchmark where only one in ten training cells is a real treated cell, a CellFlux-style model recovers none of the perturbation effect on morphology. Responder weighting recovers 83% of it, close to the 89% of an oracle given the true labels. On real data, the proposed weighting mechanism lowers the energy distance to real treated cells by 29% on BBBC021 and 49% on RxRx1. On JUMP, where by our estimate the median perturbation changes fewer than one in five cells, it raises the distance, and only milder weighting improves the fit.