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cloud.google.com·7h ago
AI Research
arXiv CS.AI·28d ago·5 min read
Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images
TL;DR
Spatial-FAD is a novel framework that enhances few-shot anomaly detection in medical images by integrating spatial priors from Vision Foundation Models with CLIP's semantic features, achieving significant improvements in lesion localization.
✦ Why It Matters
Implement Spatial-FAD to improve lesion detection accuracy in your medical imaging projects today.
Key Takeaways
- Integrate spatial information from VFMs into existing models.
- Use sliding-window aggregation for high-resolution embeddings.
- Leverage prototype-enhanced memory for efficient few-shot learning.
- Test on benchmark datasets to validate performance improvements.
How It Works
Spatial-FAD enhances few-shot anomaly detection by combining the spatial coherence of Vision Foundation Models with the semantic alignment of CLIP. It uses a VFM-enhanced adapter to inject structural affinity into CLIP features, refining visual embeddings to align with lesion boundaries.
The sliding-window aggregation strategy generates high-resolution embeddings, improving localization granularity.