CROWN: 10M cytology images beat 100x-larger pathology foundation models
bravo_abad · x · 2026-09-19
Zheng et al. trained CROWN, a pathology foundation model, on over 10 million cytology images via self-supervised learning — no manual labels needed — targeting the visual language of isolated cells and small clusters, distinct from tissue architecture in histopathology.
On 12 public cytology datasets, CROWN reaches 0.886 mean accuracy, beating UNI (0.874, pretrained on 100M+ histopathology patches) and Virchow (0.848, a 632M-parameter model trained on 1.5M whole-slide images).
Takeaway: domain-matched pretraining data can matter more than scale for scientific foundation models.
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