Histopathology
Showing 10 of 10 projects
A vision transformer foundation model pre-trained on over 200 million pathology images for computational pathology tasks.
A PyTorch-based deep learning model for simultaneous nuclear instance segmentation and classification in histopathology images.
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.
An open-source toolkit for scalable, standardized computational pathology analysis, enabling AI and machine learning on large imaging datasets.
A vision transformer-based deep learning model for automated instance segmentation and classification of cell nuclei in histopathology images.
A foundation model-driven method for semantic cell classification in whole slide images, extending Cellpose with a WSI workflow and QuPath integration.
A deep learning model that detects mitosis in breast cancer tumor cell images to assist in tumor proliferation scoring.
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