Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
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Updated
Apr 29, 2024 - Python
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.
Tools for computational pathology
Library for Digital Pathology 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.
Deep learning library for digital pathology, with both Tensorflow and PyTorch support.
A standardized Python API with necessary preprocessing, machine learning and explainability tools to facilitate graph-analytics in computational pathology.
A vision-language foundation model for computational pathology - Nature Medicine
cGAN-based Multi Organ Nuclei Segmentation
AI-based pathology predicts origins for cancers of unknown primary - Nature
Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22, CVPR'22, MICCAI'23, Histopathology'23, MICCAI'24]
Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024
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
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
Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction - CVPR 2024
🔥 🚀 Blazingly fast pipeline for patch-based classification in whole slide images
Transcriptomics-guided Slide Representation Learning in Computational Pathology - CVPR 2024
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