Open source tools for computational pathology - Nature BME
QuPath - Open-source bioimage analysis for research
Pathology Foundation Model - Nature Medicine
Cancer metastasis detection with neural conditional random field (NCRF)
Toolkit for large-scale whole-slide image processing.
The PatchCamelyon (PCam) deep learning classification benchmark.
Vision-Language Pathology Foundation Model - Nature Medicine
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Library for Digital Pathology Image Processing
Tools for computational pathology
GigaTIME: Multimodal AI generates virtual population for tumor microenvironment modeling (Cell)
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.
Powerful, open-source AI tools for digital pathology.
Multimodal Whole Slide Foundation Model for Pathology - Nature Medicine
Fusing Histology and Genomics via Deep Learning - IEEE TMI
A standardized Python API with necessary preprocessing, machine learning and explainability tools to facilitate graph-analytics in computational pathology.
Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22, CVPR'22, MICCAI'23, Histopathology'23, MICCAI'24, MICCAI'26]
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images - ICCV 2021
List of pathology feature extractors and foundation models
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