Cytology slides are essential tools in diagnosing and staging cancer, but their analysis is time-consuming and costly. Foundation models have shown great potential to assist in these tasks. In this paper, we explore how existing foundation models can be applied to cytological classification. More particularly, we focus on low-rank adaptation, a parameter-efficient fine-tuning method suited to few-shot learning. We evaluated five foundation models across four cytological classification datasets. Our results demonstrate that fine-tuning the pre-trained backbones with LoRA significantly improves model performance compared to fine-tuning only the classifier head, achieving state-of-the-art results on both simple and complex classification tasks while requiring fewer data samples.

翻译：细胞学切片是诊断和分期癌症的重要工具，但其分析过程耗时且成本高昂。基础模型在这些任务中展现出巨大的应用潜力。本文探讨了如何将现有基础模型应用于细胞学分类任务，特别聚焦于适用于少样本学习的参数高效微调方法——低秩自适应技术。我们在四个细胞学分类数据集上评估了五种基础模型。实验结果表明，相较于仅微调分类器头部，使用LoRA对预训练主干网络进行微调能显著提升模型性能，在简单和复杂分类任务上均达到最先进水平，同时所需数据样本量更少。