P3CA: Encoder-Agnostic Interpretation of Vision Foundation Model Embeddings via Spatial Probing
arXiv:2608.10131v1 Announce Type: new Abstract: 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), an encoder-agnostic method for local probing of channel-rich spatial tensors. Given a user-selected spatial prompt, P3CA estimates the feature normalization and dominant covariance directions within that region, then applies the resulting projection to the full tensor to visualize where locally informative directions are expressed. This produces a region-conditioned representation lens without modifying the encoder, retraining, or requiring task-specific labels. We implement P3CA in EmbedVision, an interactive 3D Slicer-based workflow, and evaluate it across natural images, colorectal pathology foundation-model embeddings, and spatial transcriptomic tensors. Across these settings, prompted projections reveal local structure suppressed by global PCA, improve prompt-matched pathology discrimination from frozen three-dimensional projections, and support comparison between learned and measured spatial representations.
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[Submitted on 10 Aug 2026]
Title:P3CA: Encoder-Agnostic Interpretation of Vision Foundation Model Embeddings via Spatial Probing
View a PDF of the paper titled P3CA: Encoder-Agnostic Interpretation of Vision Foundation Model Embeddings via Spatial Probing, by Amoon Jamzad and 4 other authors
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Abstract: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), an encoder-agnostic method for local probing of channel-rich spatial tensors. Given a user-selected spatial prompt, P3CA estimates the feature normalization and dominant covariance directions within that region, then applies the resulting projection to the full tensor to visualize where locally informative directions are expressed. This produces a region-conditioned representation lens without modifying the encoder, retraining, or requiring task-specific labels. We implement P3CA in EmbedVision, an interactive 3D Slicer-based workflow, and evaluate it across natural images, colorectal pathology foundation-model embeddings, and spatial transcriptomic tensors. Across these settings, prompted projections reveal local structure suppressed by global PCA, improve prompt-matched pathology discrimination from frozen three-dimensional projections, and support comparison between learned and measured spatial representations.
Comments: 10 pages, 5 figures, 1 table. Accepted at the iMIMIC Workshop, MICCAI 2026. This arXiv version is the pre-peer-review author manuscript
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2608.10131 [cs.CV]
(or arXiv:2608.10131v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.10131
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Amoon Jamzad [view email] [v1] Mon, 10 Aug 2026 18:42:38 UTC (11,665 KB)
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