Hierarchical supervision in DINOv2 training improves generalizability on white blood cell images

• Hierarchical supervision in DINOv2 improves the latent space for WBC classification. • Biologically informed hierarchy reduces error severity and aligns features. • The clustering is improved even for non-leukocytic out-of-domain datasets. • Hierarchy supports labels with different degrees of precision. • Hierarchical supervision in DINOv2 training improves generalizability on white blood cell images. The microscopic observation of blood cells is a crucial step in diagnosing pathologies such as leukemia. DINOv2 models have been employed to extract features from blood cell images, but they do not include biological knowledge, nor do they allow multi-granular labels. To enhance the representation of these cells, we propose leveraging a biologically informed hierarchy of white blood cell types. We train a DINOv2-based foundation model with a semi-supervised framework that uses hierarchical supervision. It enables using datasets with varying levels of label precision within a structure that represents the process of cell differentiation. To support multi-level label precision, we modify the original hierarchical loss function, allowing any hierarchy level to serve as a ground truth class. We evaluate our model on three external datasets, including an out-of-domain set of cervical cells. Our approach improves generalization of the model to new datasets, improving by 1 percentage point the balanced accuracy on the two blood cell external datasets, and by 2.5 percentage point the balanced accuracy on the out-of-domain dataset. In addition the proposed strategy better aligns the model’s latent space with biological properties, leading to more acceptable misclassifications