  In hematology, computational models offer significant potential to improve
diagnostic accuracy, streamline workflows, and reduce the tedious work of
analyzing single cells in peripheral blood or bone marrow smears. However,
clinical adoption of computational models has been hampered by the lack of
generalization due to large batch effects, small dataset sizes, and poor
performance in transfer learning from natural images. To address these
challenges, we introduce DinoBloom, the first foundation model for single cell
images in hematology, utilizing a tailored DINOv2 pipeline. Our model is built
upon an extensive collection of 13 diverse, publicly available datasets of
peripheral blood and bone marrow smears, the most substantial open-source
cohort in hematology so far, comprising over 380,000 white blood cell images.
To assess its generalization capability, we evaluate it on an external dataset
with a challenging domain shift. We show that our model outperforms existing
medical and non-medical vision models in (i) linear probing and k-nearest
neighbor evaluations for cell-type classification on blood and bone marrow
smears and (ii) weakly supervised multiple instance learning for acute myeloid
leukemia subtyping by a large margin. A family of four DinoBloom models (small,
base, large, and giant) can be adapted for a wide range of downstream
applications, be a strong baseline for classification problems, and facilitate
the assessment of batch effects in new datasets. All models are available at
github.com/marrlab/DinoBloom.
