TIAToolbox is a computational pathology toolbox developed by the TIA Centre. It provides an end-to-end API for pathology image analysis using best practices. Based on PyTorch, a popular deep learning framework, TIAToolbox enables efficient and flexible implementation of state-of-the-art algorithms. It supports many features through a command-line interface and can integrate with standard PyTorch modules. The toolbox offers tools for data loading, pre-processing, model inference, post-processing, and visualization. Whether you are a computational, biomedical, or clinical researcher, TIAToolbox can help you get started in digital pathology with minimal effort.
This package is designed for those interested in digital pathology, including graduate students, medical staff, members of the TIA Centre and PathLAKE, and anyone who may find it useful. We will continue to improve this package, taking into account developments in pathology, microscopy, computing, and related disciplines. Please send comments and feedback to [email protected].
If you want to contribute to this repository, please first look at our Wiki and our contributor guidelines. Also, see the Prepare for development section of this document.
The bash shell is available on all commonly encountered platforms. Commands in this README are in bash. Windows users can use the command prompt to install conda and Python packages.
conda is a management system for software packages and virtual environments. To get conda, download Anaconda, which includes hundreds of the most useful Python packages, using 2GB of disk space. Alternatively, miniconda uses 400MB, and packages can be added as needed.
- Click here for jupyter notebooks, hosted on the web, with demos of
tiatoolbox. All necessary resources to run the notebooks are remotely provided, so you don't need to have Python installed on your computer. - Click on a filename with suffix
.ipynband the notebook will open in your browser. - Click on one of the two blue checkboxes in your browser window labelled either Open in Colab or Open in Kaggle: colab and kaggle are websites providing free-of-charge platforms for running jupyter notebooks.
- Operate the notebook in your browser, editing, inserting or deleting cells as desired.
- Changes you make to the notebook will last no longer than your colab or kaggle session.
If you wish to use our programs, perhaps without developing them further, run the command pip install tiatoolbox or pip install --ignore-installed --upgrade tiatoolbox to upgrade from an existing installation.
Detailed installation instructions can be found in the documentation.
To understand better how the programs work, study the jupyter notebooks referred to under the heading Examples Taster.
tiatoolbox supports various features through command line. For more information, please try tiatoolbox --help
Prepare a computer as a convenient platform for further development of the Python package tiatoolbox and related programs as follows.
- Install pre-requisite software
- Open a terminal window
$ cd <future-home-of-tiatoolbox-directory>- Download a complete copy of the
tiatoolbox.
$ git clone https://github.com/TissueImageAnalytics/tiatoolbox.git- Change directory to
tiatoolbox
$ cd tiatoolbox- Create virtual environment for TIAToolbox using
$ conda create -n tiatoolbox-dev python=3.9 # select version of your choice
$ conda activate tiatoolbox-dev
$ pip install -r requirements/requirements_dev.txtor
$ conda env create -f requirements/requirements.dev.conda.yml # for linux/mac only.
$ conda activate tiatoolbox-dev- To use the packages installed in the environment, run the command:
$ conda activate tiatoolbox-devThe source code TIA Toolbox (tiatoolbox) as hosted on GitHub is released under the BSD-3-Clause license. The full text of the licence is included in LICENSE.
Models weights are dependent on the datasets that they were trained on. Please refer to the documentation for more details.
If you find TIAToolbox useful or use it in your research, please consider citing our paper:
@article{
Pocock2022,
author = {Pocock, Johnathan and Graham, Simon and Vu, Quoc Dang and Jahanifar, Mostafa and Deshpande, Srijay and Hadjigeorghiou, Giorgos and Shephard, Adam and Bashir, Raja Muhammad Saad and Bilal, Mohsin and Lu, Wenqi and Epstein, David and Minhas, Fayyaz and Rajpoot, Nasir M and Raza, Shan E Ahmed},
doi = {10.1038/s43856-022-00186-5},
issn = {2730-664X},
journal = {Communications Medicine},
month = {sep},
number = {1},
pages = {120},
publisher = {Springer US},
title = {{TIAToolbox as an end-to-end library for advanced tissue image analytics}},
url = {https://www.nature.com/articles/s43856-022-00186-5},
volume = {2},
year = {2022}
}