LEMON通过自监督学习实现细胞核形态的高效表征，助力病理学精准分析。
LEMON: a foundation model for nuclear morphology in Computational Pathology
- 基于海量细胞图像自监督训练，学习细胞核形态通用表征
- 在五个基准数据集上表现优异，支持多种病理分析任务
- 适合需要细胞级表征的病理研究与癌症分类任务
计算病理学依赖有效的表征学习推动癌症研究与精准医疗。尽管自监督学习已在切片和全幻灯片层面取得进展，单细胞层面的表征学习仍相对不足，但对细胞类型和细胞表型的刻画至关重要。我们提出LEMON（Learning Embeddings from Morphology Of Nuclei），一个用于可扩展单细胞图像表征学习的自监督基础模型。该模型在来自多种组织和癌种的数百万张细胞图像上训练，学习到鲁棒且通用的形态表征，支持大规模病理学单细胞分析。我们在五个基准数据集上评估了LEMON，涵盖多种预测任务，结果表明其表现强劲，凸显其作为细胞级计算病理新范式的潜力。模型权重可在https://huggingface.co/aliceblondel/LEMON获取。
原文摘要 · Abstract (English)
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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