DU Intelligence Artificielle et Santé (CHU de Lille). Séminaire 4: Traitement de l'image.
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Updated
Apr 7, 2023 - Jupyter Notebook
DU Intelligence Artificielle et Santé (CHU de Lille). Séminaire 4: Traitement de l'image.
stain augmentation method based on stainmix up using multiple known stain matrices.
This repository is dedicated to the segmentation of histological features in regenerated vascular tissues obtained from tissue-engineered vascular grafts (TEVGs) implanted in sheep.
SpatialVisVR is a VR platform tailored for advanced visualization and analysis of medical images in immuno-oncology. It allows real-time capture and comparison of mIF and mIHC images via mobile devices. Leveraging deep learning, it matches and displays similar images, supporting up to 100 protein channels.
CLI tool to run specimen-level inference on whole slide images
Scripts used for the "Glycophorin C in carotid atherosclerotic plaque reflects intraplaque hemorrhage and pre-procedural neurological symptoms" paper.
A Nextflow wrapper for HistoQC
tubular structure segmentation in histopathological images
Custom groovy scripts for QuaPath
A simple python program to organize a digital slide collection as an SQLite database
Whole Slide Image Stain Normalisation and Augmentation
Awesome List of Digital and Computational Pathology Resources
Install the GeoMxNGSPipeline_Linux_2.0.0.16 tool from Nanostring/illumina to docker container, and convert it to Singularity.
Official PyTorch Implementation of paper 'Edge-Based Graph Neural Networks for Cell-Graph Modeling and Prediction'
Python 3 Package for optimally sampling big images with texture-aware patchification based on SLIC superpixels. So Sleek !
Convenient interface for generating tiles from Aperio SVS format images
Quantitatively evaluate tumor stroma reaction within ovarian cancers, and establish assocaitaions to prognosis, molecular signatures.
Python-based pipeline for tiles generation and pre-processing from digitized histopathological images.
PathoPatcher is a Python project designed for accelerating Whole Slide Image Preprocessing, employing AI-based preprocessing techniques with features like annotation handling, color normalization, and configurable parameters
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