Pathology Foundation Model - Nature Medicine
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Pathology Foundation Model - Nature Medicine
Clinical Histopathology Imaging Evaluation Foundation Model
Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data
Vision-Language Pathology Foundation Model - Nature Medicine
Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.
C library for reading virtual slide images
Tools for computational pathology
Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.
AI-based pathology predicts origins for cancers of unknown primary - Nature
Interoperable web-based DICOM slide microscopy viewer and annotation tool
⚡ Open-source software for deep learning-based digital pathology
Python package for image-based profiling
NeuroAnatomy Toolbox: An R package for the (3D) visualisation and analysis of biological image data, especially tracings of single neurons.
High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations
Predicting Cell Health with Morphological Profiles
A foundation model-driven whole slide image-scale cell phenotyping method with QuPath integration Resources
A flexible Julia toolkit for high-dimensional cellular profiles