AI Research
P3CA: Encoder-Agnostic Interpretation of Vision Foundation Model Embeddings via Spatial Probing
Unlocking localized feature interpretability in vision foundation models for medical imaging.
Vision foundation models are increasingly used as reusable encoders in medical image computing, yet their high-dimensional spatial embeddings are difficult to inspect beyond downstream task performance or global dimensionality reduction. We propose position-prompted PCA (P3CA), a...
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ArXiv CS.CV
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ArXiv CS.CV
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P3CA provides a critical diagnostic bridge between black-box vision foundation models and clinical application by enabling precise, local interpretation of complex spatial embeddings. By eliminating the need for retraining, this method allows researchers to unlock latent spatial insights directly from frozen models.
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