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About this paper
Dibaloke Chanda, Praveen Madiraju
MOSAIC maps features from pathology encoders into a shared 64-dimensional space, then tests correspondence. The authors fit encoder-specific mappings on training data and evaluate held-out patch identity retrieval. In six compatible 256-pixel encoders at 10× on CPTAC, GCCA reaches Recall@1 of 0.925, versus 0.000 for unaligned truncation; this is patch matching, not diagnostic accuracy, and downstream gains vary by task.
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