LEMON: a foundation model for nuclear morphology in Computational Pathology

• URL: http://arxiv.org/abs/2603.25802v1
• Date: Thu, 26 Mar 2026 18:09:09 GMT
• Title: LEMON: a foundation model for nuclear morphology in Computational Pathology
• Abstract summary: We introduce LEMON, a self-supervised foundation model for scalable single-cell image representation learning.<n> trained on millions of cell images from diverse tissues and cancer types, LEMON learns robust and versatile morphological representations.<n>We evaluate LEMON on five benchmark datasets across a range of prediction tasks and show that it provides strong performance.
• Score: 1.125921430041183
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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