New FAME benchmark standardizes evaluation for few-shot medical image segmentation

Researchers have introduced FAME, a new benchmark designed to evaluate few-shot medical image segmentation (FS-MIS) methods. FAME standardizes evaluation across diverse approaches, including specialist models, SAM-based, CLIP-based, and multimodal large language model (MLLM) based techniques. The benchmark comprises 14,958 test samples across various anatomical sites, imaging modalities, and regions of interest, assessing models in zero-shot and ten-shot scenarios, as well as their ability to recognize target absence and generalize under different shifts. Initial findings indicate that direct visual adaptation is more effective than prompt-based strategies for few-shot segmentation, and while more support examples can improve performance, their utility depends on the model's ability to leverage them. AI

IMPACT Standardizes evaluation for medical image segmentation models, potentially accelerating development and comparison of new techniques.

RANK_REASON The item is an academic paper introducing a new benchmark for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

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• FAME
• multimodal large language model
• SAM

• paper
• other

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