A Single Reference-Guided Adaptation of Foundation Model Predictions for High-Performance Image Segmentation.

Area of Science:

• Artificial Intelligence
• Biomedical Imaging
• Machine Learning

Background:

• Foundation models (FMs) show great potential but require extensive fine-tuning for specialized domains like biomedical imaging.
• Large labeled datasets and significant computational resources are barriers to widespread FM adoption in medicine.

Purpose of the Study:

• Introduce reference-guided adaptation (RGA) for ultra-data-efficient and interpretable FM adaptation.
• Enable accurate FM predictions for specific inference samples using only a single reference example.

Main Methods:

• RGA aligns reference and inference samples using semantic relationships.
• A lightweight refinement model is trained to enhance FM predictions without altering the core FM.
• The framework was tested on medical image segmentation tasks using SAM, MedSAM, and SAM2.

Main Results:

• RGA effectively narrows the performance gap between general FM predictions and specific medical imaging segmentation needs.
• The approach demonstrates success in limited-data scenarios by leveraging single-reference task-specific knowledge.
• The method addresses the 'last-mile' challenge in deploying FMs for medical applications.

Conclusions:

• RGA offers a novel strategy for ultra-data-efficient and explainable AI modeling in biomedical imaging.
• This approach facilitates the deployment of FMs by overcoming data and computational barriers.
• RGA paves the way for more accessible and effective AI tools in medical image analysis.