Benchmarking Foundation and Large Language Models for Few-Shot Medical Image Segmentation

Jinghong Liu; Yuchuan Deng; Fanping Liu; Meng Huang; Xirong Li

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arXiv · 2607.27856

Benchmarking Foundation and Large Language Models for Few-Shot Medical Image Segmentation

Jinghong Liu·Yuchuan Deng·Fanping Liu·Meng Huang·Xirong Li

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Abstract

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.

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Jinghong Liu, Yuchuan Deng, Fanping Liu, Meng Huang, Xirong Li. 2026-08-17. Benchmarking Foundation and Large Language Models for Few-Shot Medical Image Segmentation. https://arxiv.org/abs/2607.27856

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