Open source tools for computational pathology - Nature BME

Cancer metastasis detection with neural conditional random field (NCRF)

The PatchCamelyon (PCam) deep learning classification benchmark.

Tools for computational pathology

Library for Digital Pathology Image Processing

Vision-Language Pathology Foundation Model - Nature Medicine

Toolkit for large-scale whole-slide image processing.

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.

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]

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

Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction - CVPR 2024

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

Fast and scalable search of whole-slide images via self-supervised deep learning - Nature Biomedical Engineering

Transcriptomics-guided Slide Representation Learning in Computational Pathology - CVPR 2024

A package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.

Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images - CVPR 2023