Difference between image features in Virchow vs PRISM
hello,
I was wondering if the pathology image features will differ in Virchow vs PRISM, in terms of classification performance since they both use Virchow as the image encoder?
Thanks,
Rohan
Hi Rohan,
Virchow is a tile based model, i.e. it encodes small 224x224 patches of pathology slides at 20x magnification. In a single slide there can be anything from 5,000 to 50,000 of these tiles.
Prism is a slide based model. It takes Virchow embeddings of all tiles in a slide (up to 100,000) and encodes it into a single slide level embedding. It can also generate a string with most significant findings.
So these models operate on different data and used for different tasks. If you have tile level labels - use Virchow. If you have slide level labels - use Prism.
Does this answer your question?
Best,
George
PS if you’re interested in alternative ways of deriving slide representation from Virchow tile embeddings check out this paper: https://arxiv.org/abs/2502.13027
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I am interested in patch based embeddings, so from Virchow, I just took average of all the patches. am not sure if taking slide based embeddings from PRISM would be equivalent to taking average of patches from Virchow(lets say the we are doing it for 1 patient with 1 slide with 1000 patches). Obviously, the numbers can never be exact same, but just want to know if we take patch based embeddings of 1000 patches from Virchow and average them , is equivalent to taking a Slide embedding(1000 patches) from PRISM. Basically I am working on patch based feature extraction, so if both are technically same, I would avoid using PRISM.
Prism slide embedding is not a simple average of Virchow patch embeddings. You can verify it easily yourself. Please consult the literature on slide level representation - the baseline of taking a simple average was long investigated. It’s safe to say that Prism slide embedding is better for linear probing than a simple average.