LEMON: a foundation model for nuclear morphology in Computational Pathology
This work addresses the underexplored problem of single-cell representation learning in computational pathology, providing a scalable foundation model for large-scale cell analysis.
LEMON is a self-supervised foundation model for single-cell nuclear morphology representation learning, trained on millions of cell images from diverse tissues and cancer types. It achieves strong performance across five benchmark datasets, establishing a new paradigm for cell-level computational pathology.
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.