TIAToolbox: An End-to-End Toolbox for Advanced Tissue Image Analytics
Authors:
- Johnathan PocockJPJohnathan Pocock
- Simon GrahamSGSimon Graham
- Quoc Dang VuQVQuoc Dang Vu
- Mostafa JahanifarMJMostafa Jahanifar
- Srijay DeshpandeSDSrijay Deshpande
- Giorgos HadjigeorghiouGHGiorgos Hadjigeorghiou
- Adam ShephardASAdam Shephard
- Raja Muhammad Saad BashirRBRaja Muhammad Saad Bashir
- Mohsin BilalMBMohsin Bilal
- Wenqi LuWLWenqi Lu
- David EpsteinDEDavid Epstein
- Fayyaz MinhasFMFayyaz Minhas
Preprint
Abstract
Computational Pathology (CPath) has seen rapid growth in recent years, driven by advanced deep learning (DL) algorithms. These algorithms typically share the same sequence of steps. However, due to the sheer size and complexity of handling large multi-gigapixel whole-slide images, there is no open-source software library that provides a generic end-to-end API for pathology image analysis using best practices for CPath. Most researchers have designed custom pipelines from the bottom-up, restricting the development of advanced CPath algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make CPath more accessible to new and advanced CPath scientists and pathologists alike. We provide a usable and adaptable library with efficient, cutting-edge and unit-tested tools for data loading, pre-processing, model inference, post-processing and visualization. This enables all kinds of users to easily build upon recent DL developments in the CPath literature. TIAToolbox provides a user-friendly modular API to enable seamless integration of advanced DL algorithms. We show with the help of examples how state-of-the-art DL algorithms can be streamlined using TIAToolbox.Keywords
All Related Versions
- Published version: Communications Medicine, 2 (1), 1.
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