基准测试用于小样本医学图像分割的基础模型与大语言模型
Benchmarking Foundation and Large Language Models for Few-Shot Medical Image Segmentation
AI总结：
该研究推出统一基准FAME，涵盖四类模型，含14958个样本，评估得出小样本分割的关键规律，助力开发更有效的小样本医学分割方法。
AI中文摘要：
小样本医学图像分割（FS-MIS）旨在通过少量带标注的支持样本来分割新的感兴趣区域（ROIs）。尽管该领域进展迅速，但现有FS-MIS解决方案涵盖多种范式，却在不一致的设置下进行评估，导致其相对有效性尚不明确。我们推出FAME，这是一个用于评估FS-MIS解决方案的统一基准，涵盖专家模型、基于SAM的方法、基于CLIP的方法以及基于多模态大语言模型（MLLM）的方法。FAME包含来自7个解剖部位、9种成像模态和14个ROI类别的14958个测试样本，在零样本和十样本设置下评估模型，还额外评估目标缺失识别以及协变量和语义偏移下的泛化能力。我们的评估得出多项发现：第一，有效的小样本分割取决于模型利用支持样本的方式：直接视觉适应通常优于基于提示的策略；第二，仅当模型能有效利用支持样本时，增加支持样本数量才会提升性能；第三，语义迁移仍比成像域适应更具挑战性，且强大的定位能力并不一定意味着可靠的目标缺失识别。我们希望FAME能帮助人们全面了解当前的FS-MIS解决方案，并推动开发更有效、更可靠的小样本医学分割方法。
英文摘要：
Few-shot medical image segmentation (FS-MIS) aims to segment novel regions of interest (ROIs) from a few annotated support examples. Despite rapid progress, existing FS-MIS solutions span diverse paradigms but are evaluated under inconsistent settings, leaving their relative effectiveness unclear. We introduce FAME, a unified benchmark for evaluating FS-MIS solutions, covering specialists, SAM-based methods, CLIP-based methods, and MLLM-based methods. FAME contains 14,958 test samples across 7 anatomical sites, 9 imaging modalities, and 14 ROI categories, and evaluates models under zero-shot and ten-shot settings with additional assessment of target-absence recognition and generalization under covariate and semantic shifts. Our evaluation reveals several findings. First, effective few-shot segmentation depends on how models exploit support examples: direct visual adaptation generally outperforms prompt-based strategies. Second, increasing support examples improves performance only when models can effectively utilize them. Third, semantic transfer remains substantially more challenging than imaging-domain adaptation, and strong localization ability does not necessarily imply reliable target-absence recognition. We hope FAME provides a comprehensive understanding of current FS-MIS solutions and facilitates the development of more effective and reliable few-shot medical segmentation methods.
