Computational pathology relies on effective representation learning to support cancer research and precision medicine. Although self-supervised learning has driven major progress at the patch and whole-slide image levels, representation learning at the single-cell level remains comparatively underexplored, despite its importance for characterizing cell types and cellular phenotypes. We introduce LEMON (Learning Embeddings from Morphology Of Nuclei), a self-supervised foundation model for scalable single-cell image representation learning. Trained on millions of cell images from diverse tissues and cancer types, LEMON learns robust and versatile morphological representations that support large-scale single-cell analyses in pathology. We evaluate LEMON on five benchmark datasets across a range of prediction tasks and show that it provides strong performance, highlighting its potential as a new paradigm for cell-level computational pathology. Model weights are available at https://huggingface.co/aliceblondel/LEMON.
@article{arxiv.2603.25802,
title = {LEMON: a foundation model for nuclear morphology in Computational Pathology},
author = {Loïc Chadoutaud and Alice Blondel and Hana Feki and Jacqueline Fontugne and Emmanuel Barillot and Thomas Walter},
journal= {arXiv preprint arXiv:2603.25802},
year = {2026}
}