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A whole-slide foundation model for digital pathology from real-world data

634
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June 6, 2024
Published Date

Research Abstract & Technology Focus

AbstractDigital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1–3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context4. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data6. With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision–language pretraining for pathology7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.
whole-slide foundation model digital pathology real-world
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What is the core focus of the research titled 'A whole-slide foundation model for digital pathology from real-world data'?

This literature focuses on: AbstractDigital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1–3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing ...

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Yes, open-source projects like PKU-YuanGroup/Helios (Helios: Real Real-Time Long Video Generation Model) are actively building upon these concepts.

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Yes, highly correlated activity was mapped. An entry titled 'A whole-slide foundation model for digital pathology from real-world data' discusses this: AbstractDigital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1–3. Pr...

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Yes, highly correlated activity was mapped. An entry titled 'Generative AI for misalignment-resistant virtual staining to accelerate histopathology workflows' discusses this: Ma, Li, and colleagues present a virtual tissue staining method that overcomes data mismatch by separating image generation from spatial alignment....

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