统一分割结直肠癌多模态影像，高效适配不同数据类型。
CRC-SAM: SAM-Based Multi-Modal Segmentation and Quantification of Colorectal Cancer in CT, Colonoscopy, and Histology Images
- 基于MedSAM框架，用轻量级LoRA层适配不同医学影像模态。
- 在三种数据集上均优于现有方法，尤其在小样本模态表现突出。
- 适合临床全流程使用，特别适用于数据稀缺的影像分析场景。
我们提出CRC-SAM，一种统一的结直肠癌分割框架，可处理结肠镜、CT及组织病理图像。不同于以往单模态方法，CRC-SAM在整个临床流程中提供一致且模态无关的分割结果。该框架以MedSAM为基础，将低秩适应（LoRA）层引入冻结编码器，实现对低资源模态的高效域迁移，仅需极少可训练参数。在MSD-Colon、CVC-ClinicDB和EBHI-Seg数据集上的实验表明，其跨模态性能显著优于现有先进方法，验证了基于基础模型的轻量级LoRA适配在结直肠癌分析中的有效性。
原文摘要 · Abstract (English)
We present CRC-SAM, a unified framework for colorectal cancer segmentation across colonoscopy, CT, and histopathology images. Unlike prior single-modality methods, CRC-SAM provides consistent, modality-agnostic segmentation throughout the clinical workflow. Built on MedSAM, it incorporates low-rank adaptation (LoRA) layers into a frozen encoder, enabling efficient domain transfer to underrepresented modalities with minimal trainable parameters. Experiments on MSD-Colon, CVC-ClinicDB, and EBHI-Seg demonstrate superior performance across modalities, outperforming state-of-the-art baselines and highlighting the effectiveness of lightweight LoRA adaptation for foundation-model-based colorectal cancer analysis.
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。