Digital Pathology
Showing 6 of 6 projects
A general-purpose foundation model for cancer diagnosis and prognosis prediction from histopathology whole-slide images.
A whole-slide foundation model for digital pathology, pre-trained on real-world data to analyze tissue slides at tile and slide levels.
A sliding window framework for classifying high-resolution whole-slide microscopy and histopathology images using deep neural networks.
A C library for reading whole slide image files (virtual slides) with a consistent API across multiple vendor formats.
A vision-language foundation model for computational pathology, pretrained on 1.17M histopathology image-caption pairs for diverse AI tasks.
An open-source toolkit for scalable, standardized computational pathology analysis, enabling AI and machine learning on large imaging datasets.
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