TIAViz: A Browser-based Visualization Tool for Computational Pathology
  Models
Mark Eastwood John Pocock Mostafa Jahanifar Adam Shephard Skiros Habib Ethar Alzaid Abdullah Alsalemi Jan Lukas Robertus Nasir Rajpoot Shan Raza Fayyaz Minhas
Digital pathology has gained significant traction in modern healthcare
systems. This shift from optical microscopes to digital imagery brings with it
the potential for improved diagnosis, efficiency, and the integration of AI
tools into the pathologists workflow. A critical aspect of this is
visualization. Throughout the development of a machine learning (ML) model in
digital pathology, it is crucial to have flexible, openly available tools to
visualize models, from their outputs and predictions to the underlying
annotations and images used to train or test a model. We introduce TIAViz, a
Python-based visualization tool built into TIAToolbox which allows flexible,
interactive, fully zoomable overlay of a wide variety of information onto whole
slide images, including graphs, heatmaps, segmentations, annotations and other
WSIs. The UI is browser-based, allowing use either locally, on a remote
machine, or on a server to provide publicly available demos. This tool is open
source and is made available at:
https://github.com/TissueImageAnalytics/tiatoolbox and via pip installation
(pip install tiatoolbox) and conda as part of TIAToolbox.