Digital Pathology
Showing 11 of 11 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 vision-language foundation model for computational pathology, pretrained on 1.17M histopathology image-caption pairs for diverse AI tasks.
A C library for reading whole slide image files (virtual slides) with a consistent API across multiple vendor formats.
An open-source toolkit for scalable, standardized computational pathology analysis, enabling AI and machine learning on large imaging datasets.
A curated list of software, tools, pipelines, and plugins for image analysis in biological research.
A web-based DICOM slide microscopy viewer and annotation tool for imaging data science and computational pathology.
Open-source software for deep learning-based analysis and visualization of whole slide images in digital pathology.
A Python library for interacting with QuPath, providing a pythonic interface to manage and analyze digital pathology projects.
A foundation model-driven method for semantic cell classification in whole slide images, extending Cellpose with a WSI workflow and QuPath integration.
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