# LEMON: a foundation model for nuclear morphology in Computational Pathology

arXiv: https://arxiv.org/abs/2603.25802 · HTML: https://mlsanity.com/paper/2603.25802/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 39 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 26 of 26)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [iBOT: Image BERT Pre-Training with Online Tokenizer](https://mlsanity.com/paper/2111.07832/) (2021)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Multimodal Whole Slide Foundation Model for Pathology](https://mlsanity.com/paper/2411.19666/) (2024)
- [Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology](https://mlsanity.com/paper/2408.00738/) (2024)
- [Benchmarking Self-Supervised Learning on Diverse Pathology Datasets](https://mlsanity.com/paper/2212.04690/) (2022)
- [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020)
- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [Molecular-driven Foundation Model for Oncologic Pathology](https://mlsanity.com/paper/2501.16652/) (2025)
- [Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach](https://mlsanity.com/paper/2405.15613/) (2024)
- [Multistain Pretraining for Slide Representation Learning in Pathology](https://mlsanity.com/paper/2408.02859/) (2024)
- [DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology](https://mlsanity.com/paper/2404.05022/) (2024)
- [Giga-SSL: Self-Supervised Learning for Gigapixel Images](https://mlsanity.com/paper/2212.03273/) (2022)
- [Negative Sampling for Contrastive Representation Learning: A Review](https://mlsanity.com/paper/2206.00212/) (2022)
- [CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models](https://mlsanity.com/paper/2501.05269/) (2025)
- [A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification](https://mlsanity.com/paper/2407.12210/) (2024)
- [Rotation-Agnostic Image Representation Learning for Digital Pathology](https://mlsanity.com/paper/2311.08359/) (2023)
- [Augmentations vs Algorithms: What Works in Self-Supervised Learning](https://mlsanity.com/paper/2403.05726/) (2024)
- [VOLTA: an Environment-Aware Contrastive Cell Representation Learning for Histopathology](https://mlsanity.com/paper/2303.04696/) (2023)
- [Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology](https://mlsanity.com/paper/2503.18709/) (2025)

## Abstract (arXiv)

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.