pathology

Open source tools for computational pathology - Nature BME

QuPath - Open-source bioimage analysis for research

Cancer metastasis detection with neural conditional random field (NCRF)

Pathology Foundation Model - Nature Medicine

The PatchCamelyon (PCam) deep learning classification benchmark.

C library for reading virtual slide images

Tools for computational pathology

Library for Digital Pathology Image Processing

Vision-Language Pathology Foundation Model - Nature Medicine

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.

Toolkit for large-scale whole-slide image processing.

Powerful, open-source AI tools for digital pathology.

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.

Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images - ICCV 2021

Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22, CVPR'22, MICCAI'23, Histopathology'23, MICCAI'24]

cGAN-based Multi Organ Nuclei Segmentation

AI-based pathology predicts origins for cancers of unknown primary - Nature

Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks - MICCAI 2021

List of pathology feature extractors and foundation models