QuPath - Bioimage analysis & digital pathology

Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering

Cancer metastasis detection with neural conditional random field (NCRF)

The PatchCamelyon (PCam) deep learning classification benchmark.

C library for reading virtual slide images

Tools for computational pathology

Library for Digital Pathology Image Processing

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 library for digital pathology, with both Tensorflow and PyTorch support.

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.

cGAN-based Multi Organ Nuclei Segmentation

Towards a general-purpose foundation model for computational pathology - Nature Medicine

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

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

A vision-language foundation model for computational pathology - Nature Medicine

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

Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024

Evidence SARS-CoV-2 Emerged From a Biological Laboratory in Wuhan, China

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