Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
-
Updated
Apr 29, 2024 - Python
Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
QuPath - Bioimage analysis & digital pathology
Cancer metastasis detection with neural conditional random field (NCRF)
The PatchCamelyon (PCam) deep learning classification benchmark.
Tools for computational pathology
Fusing Histology and Genomics via Deep Learning - IEEE TMI
cGAN-based Multi Organ Nuclei Segmentation
Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22, CVPR'22, MICCAI'23, Histopathology'23, MICCAI'24]
A standardized Python API with necessary preprocessing, machine learning and explainability tools to facilitate graph-analytics in computational pathology.
Library for Digital Pathology Image Processing
AI-based pathology predicts origins for cancers of unknown primary - Nature
Deep learning library for digital pathology, with both Tensorflow and PyTorch support.
Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images - ICCV 2021
Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks - MICCAI 2021
Fast and scalable search of whole-slide images via self-supervised deep learning - Nature Biomedical Engineering
Towards a general-purpose foundation model for computational pathology - 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.
A package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.
Add a description, image, and links to the pathology topic page so that developers can more easily learn about it.
To associate your repository with the pathology topic, visit your repo's landing page and select "manage topics."