Topic: pathology Goto Github
Some thing interesting about pathology
Some thing interesting about pathology
pathology,Code for the im4MEC model described in the paper 'Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts'.
Organization: airmec
Home Page: https://doi.org/10.1016/S2589-7500(22)00210-2
pathology,Cancer metastasis detection with neural conditional random field (NCRF)
Organization: baidu-research
pathology,The PatchCamelyon (PCam) deep learning classification benchmark.
User: basveeling
pathology,A standardized Python API with necessary preprocessing, machine learning and explainability tools to facilitate graph-analytics in computational pathology.
Organization: biomedsciai
pathology,Raccoon is a NoSQL-based medical image archive for managing the DICOM images.
Organization: cylab-tw
pathology,Tools for computational pathology
Organization: dana-farber-aios
Home Page: https://pathml.org
pathology,A package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.
Organization: diagnijmegen
Home Page: https://diagnijmegen.github.io/pathology-whole-slide-data/
pathology,a toolbox for manipulation of digital pathology slides and annotations
User: dslituiev
pathology,List of pathology feature extractors
User: georg-wolflein
pathology,Library for Digital Pathology Image Processing
Organization: histolab
Home Page: http://histolab.readthedocs.io
pathology,nuclei.io: Human-in-the-loop active learning framework for pathology image analysis
User: huangzhii
Home Page: https://huangzhii.github.io/nuclei-HAI/
pathology,Bayesian Inference of Slide-level Confidence via Uncertainty Index Thresholding
User: jamesdolezal
pathology,Repo for Tang et al, bioRxiv 454793 (2018)
Organization: keiserlab
pathology,Simple library for preprocessing histopathological whole-slide images (WSI) into tiles (a.k.a. patches) towards deep learning
User: lucasrla
pathology,Image filters for digital pathology: detect pen marks, background, and artifacts. Use them for preprocessing towards deep learning
User: lucasrla
pathology,A Hierarchical Graph V-Net with Semi-supervised Pre-training for Breast Cancer Histology Image Classification" (IEEE TMI)
User: lyhkevin
pathology,Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
Organization: mahmoodlab
Home Page: http://clam.mahmoodlab.org
pathology,A vision-language foundation model for computational pathology - Nature Medicine
Organization: mahmoodlab
pathology,Deep learning enabled assessment of cardiac allograft rejection from endomyocardial biopsies- Nature Medicine
Organization: mahmoodlab
Home Page: http://crane.mahmoodlab.org
pathology,Federated Learning for Computational Pathology - Medical Image Analysis
Organization: mahmoodlab
pathology,Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images - ICCV 2021
Organization: mahmoodlab
pathology,Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images - CVPR 2023
Organization: mahmoodlab
pathology,Multimodal prototyping for cancer survival prediction - ICML 2024
Organization: mahmoodlab
pathology,cGAN-based Multi Organ Nuclei Segmentation
Organization: mahmoodlab
pathology,Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology - CVPR 2024
Organization: mahmoodlab
pathology,Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks - MICCAI 2021
Organization: mahmoodlab
Home Page: http://mahmoodlab.org
pathology,Fusing Histology and Genomics via Deep Learning - IEEE TMI
Organization: mahmoodlab
Home Page: http://www.mahmoodlab.org
pathology,Fast and scalable search of whole-slide images via self-supervised deep learning - Nature Biomedical Engineering
Organization: mahmoodlab
pathology,Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction - CVPR 2024
Organization: mahmoodlab
pathology,Transcriptomics-guided Slide Representation Learning in Computational Pathology - CVPR 2024
Organization: mahmoodlab
pathology,AI-based pathology predicts origins for cancers of unknown primary - Nature
Organization: mahmoodlab
Home Page: http://toad.mahmoodlab.org
pathology,Towards a general-purpose foundation model for computational pathology - Nature Medicine
Organization: mahmoodlab
pathology,Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024
User: marvinler
pathology,Deep-Learning for Tidemark Segmentation in Human Osteochondral Tissues Imaged with Micro-computed Tomography
Organization: mipt-oulu
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]
Organization: nadeemlab
Home Page: https://deepliif.org
pathology,C library for reading virtual slide images
Organization: openslide
Home Page: https://openslide.org/
pathology,Data and code to accompany our Nature publication
Organization: path-ai
Home Page: https://www.nature.com/articles/s41467-021-21896-9
pathology,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.
Organization: pathologyfoundation
pathology,Digital Pathology Whole Slide Image Analysis Toolbox
User: pingjunchen
Home Page: https://pyslide.readthedocs.io
pathology,Whole Slide Digital Pathology Image Tissue Localization
User: pingjunchen
Home Page: https://tissueloc.readthedocs.io
pathology,Evidence SARS-CoV-2 Emerged From a Biological Laboratory in Wuhan, China
User: project-evidence
Home Page: https://project-evidence.github.io
pathology,MONAI Deploy aims to become the de-facto standard for developing, packaging, testing, deploying and running medical AI applications in clinical production.
Organization: project-monai
pathology,QuPath - Bioimage analysis & digital pathology
Organization: qupath
Home Page: https://qupath.github.io
pathology,Brazilian Agricultural Research Corporation (EMBRAPA) fully annotated dataset for plant diseases. Plug and play installation over PiP.
User: rodrigobressan
pathology,๐ฅ ๐ Blazingly fast pipeline for patch-based classification in whole slide images
Organization: sbu-bmi
Home Page: https://wsinfer.readthedocs.io
pathology,Python 3 library for the augmentation & normalization of H&E images
User: sebastianffx
pathology,Powerful, open-source AI tools for digital pathology.
Organization: slideflow
Home Page: https://slideflow.dev
pathology,Re-stained whole slide image alignment
User: smujiang
pathology,CoRA Docs
Organization: spawaskar-cora
pathology,DLBCL-Morph dataset containing high resolution tissue microarray scans from 209 DLBCL cases, with geometric features computed using deep learning
Organization: stanfordmlgroup
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