QuPath - Bioimage analysis & digital pathology
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Updated
Jul 17, 2024 - Java
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.
Tools for computational pathology
Library for Digital Pathology Image Processing
Fusing Histology and Genomics via Deep Learning - IEEE TMI
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.
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]
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images - ICCV 2021
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
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