QuPath - Bioimage analysis & digital pathology
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Updated
May 25, 2024 - Java
QuPath - Bioimage analysis & digital pathology
The official deployment of the Digital Slide Archive and HistomicsTK.
A Python toolkit for pathology image analysis algorithms.
Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
C++ library and command-line software for processing and analysis of terabyte-scale volume images locally or on a computing cluster.
A vision-language foundation model for computational pathology - Nature Medicine
A Fiji plugin that automatically quantify synapses from multi-channel fluorescence microscopy images.
SIMPLI is a highly configurable pipeline for the analysis of multiplexed imaging data.
Fast and scalable search of whole-slide images via self-supervised deep learning - Nature Biomedical Engineering
CellOrganizer on Jupyter Notebook
Track single-cells and profile the cell cycle with PCNA images.
cialab/DeepSlides fork to make it work with newer Python libraries.
Bio- and biomedical imaging dataset for machine learning and deep learning (for ExperimentHub in Bioconductor)
AI-based pathology predicts origins for cancers of unknown primary - Nature
🐳 Script to build a Singularity image for CellOrganizer
Analysis of single molecule localization microscopy. 'pointpattern': statistical analysis. 'image': image processing+analysis+classification
CellOrganizer for Docker
ViCAR extracts and employs (Vi)sual (C)ues for an (A)daptive (R)egistration of time-lapse image data recorded in microfluidic devices.
SeeVIS is a (S)egmentation-fr(ee) (VIS)ualization pipeline for time-lapse image data. It comprises three steps: 1. preprocessing, 2. feature extraction, and 3. an extended version of the space time cube with three novel color mappings adapted to cell colony growth.
CYCASP is a methodology for investigating and understanding (C)olon(Y) growth and (C)ell (A)ttributes at the population level. It couples (SP)atiotemporal changes by relying on two novel data abstractions and a modular algorithm.
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