无需提示词的轻量级模型，高效分割病理切片细胞核
Prompt-Free Lightweight SAM Adaptation for Histopathology Nuclei Segmentation with Strong Cross-Dataset Generalization
- 不依赖提示词，仅微调LoRA模块，参数仅410万
- 在三个数据集上表现超越现有方法，跨数据集泛化强
- 适合资源有限的医疗场景快速部署
病理组织细胞核分割对定量组织分析和癌症诊断至关重要。现有分割方法虽性能优异，但计算开销大且跨数据集泛化能力弱，限制了实际应用。基于SAM的方法在通用与医学图像中展现潜力，但通常依赖提示或复杂解码器，难以适配密集分布、形态多样的病理图像。本文提出一种无需提示词、轻量化的SAM适配框架，利用多层级编码器特征与残差解码，仅微调冻结的SAM编码器中的LoRA模块，可训练参数仅为410万。在TNBC、MoNuSeg和PanNuke三个基准数据集上的实验表明，该方法达到当前最优性能，并具备出色的跨数据集泛化能力，验证了其在病理学应用中的有效性和实用性。
原文摘要 · Abstract (English)
Histopathology nuclei segmentation is crucial for quantitative tissue analysis and cancer diagnosis. Although existing segmentation methods have achieved strong performance, they are often computationally heavy and show limited generalization across datasets, which constrains their practical deployment. Recent SAM-based approaches have shown great potential in general and medical imaging, but typically rely on prompt guidance or complex decoders, making them less suitable for histopathology images with dense nuclei and heterogeneous appearances. We propose a prompt-free and lightweight SAM adaptation that leverages multi-level encoder features and residual decoding for accurate and efficient nuclei segmentation. The framework fine-tunes only LoRA modules within the frozen SAM encoder, requiring just 4.1M trainable parameters. Experiments on three benchmark datasets TNBC, MoNuSeg, and PanNuke demonstrate state-of-the-art performance and strong cross-dataset generalization, highlighting the effectiveness and practicality of the proposed framework for histopathology applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。