Improving patient outcomes with AI-powered pathology.
Joined March 2016
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๐ฌ Moving Beyond Pixels: Advancing Multimodal Pathology
Pathology AI has long relied on images alone. What happens when you add language?
We combined PLUTO-4 vision embeddings with rich histological descriptions to build a joint vision-language space for disease classification, and the results are promising.
๐งต #AI #MachineLearning #Pathology #MultimodalAI #FoundationModels
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Looking deeper into the the shared embedding space, it has clinically meaningful structure:
๐ข Subtypes cluster together
๐ค Inflammatory dermatoses separate cleanly from melanocytic lesions
Slide embeddings mirror text embedding organization, showing alignment across modalities, not just within them.
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Whatโs next? Language unlocks potential capabilities beyond accuracy:
๐ Open-vocabulary prediction: extend to new diagnoses with minimal tuning.
๐ Natural language slide search: e.g. "slides showing interface dermatitis with basal vacuolization"
๐ฉบ Further enrichment with case-specific clinical context
Full blog post: pathai.com/blog/advancing-muโฆ
#DigitalPathology #ComputationalPathology #HealthcareAI #PLUTO4 #MultimodalAI
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๐ Excited to share PLUTO-4, our new state-of-the-art foundation models for pathology! ๐ฌ
Weโre seeing SoTA performance across multiple public benchmarks (EVA and HEST) โ surpassing other leading pathology foundation models. (1/6)
#AI #MachineLearning #Pathology #FoundationModels #HealthcareAI
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Beyond public benchmarks, PLUTO-4 shows real-world impact โ
๐ฉบ ~10 % improvement across multiple PathAI products, with strong gains in dermatopathology specimen classification.
These advances bring us closer to robust, generalizable FMs for pathology applications.
#Dermatology #HealthcareAI
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These results highlight how our PLUTO-4 foundation models enhance PathAIโs AI-pathology products across digital diagnostics and translational research.
Weโre excited for the new capabilities PLUTO-4 will unlock for our partners and the community!
๐ Learn more in our technical report:
๐ arxiv.org/abs/2511.02826
#AI #Pathology #HealthcareAI #FoundationModels #PLUTO4
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PathAI retweeted
4 Feb 2025
The opportunity to standardize the way we construct data sets is important...If we try to build a data set for every use case, we can set ourselves up to fail. We don't want to build a large reference data set that doesn't get used - @balasubramaniac from @Path_AI #FriendDx
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PathAI #MachineLearning engineers have
recently published new #AI findings for mechanistic interpretability of PLUTO, a pathology #foundationmodel. Using sparse autoencoders (SAEs), we uncovered biologically meaningful and interpretable features. ๐งต
arxiv.org/html/2407.10785v2
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Monosemantic representations
- Single SAE dimensions correlate with counts of single cell types. For example, SAE-1736 represents plasma cell abundance exclusively
- The findings generalized to:
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Out-of-domain datasets (CPTAC)
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Different stains (H&E, IHC)
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Various scanners
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๐ Interpretable concepts found using SAE
- SAE trained on PLUTO embeddings disentangled polysemantic features. Single dimensions captured distinct concepts:
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Cell types (e.g., cancer cells, red blood cells)
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Geometric features (e.g. edge of tissue)
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Artifacts (surgical ink)
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๐ฌ Impact
This study shows the promise & potantial of SAEs in explaining foundation model behavior for medical imaging. Interpretable features unlock:
- Potential for clinical AI ๐ฅ
- New biological insights ๐งช
๐ Read the full work: bit.ly/4gl20xZ
#AI #Pathology
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