TIAViz: A Browser-based Visualization Tool for Computational Pathology Models
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866929244522676224 |
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| author | Eastwood, Mark Pocock, John Jahanifar, Mostafa Shephard, Adam Habib, Skiros Alzaid, Ethar Alsalemi, Abdullah Robertus, Jan Lukas Rajpoot, Nasir Raza, Shan Minhas, Fayyaz |
| author_facet | Eastwood, Mark Pocock, John Jahanifar, Mostafa Shephard, Adam Habib, Skiros Alzaid, Ethar Alsalemi, Abdullah Robertus, Jan Lukas Rajpoot, Nasir Raza, Shan Minhas, Fayyaz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09990 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TIAViz: A Browser-based Visualization Tool for Computational Pathology Models Eastwood, Mark Pocock, John Jahanifar, Mostafa Shephard, Adam Habib, Skiros Alzaid, Ethar Alsalemi, Abdullah Robertus, Jan Lukas Rajpoot, Nasir Raza, Shan Minhas, Fayyaz Image and Video Processing Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning 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. |
| title | TIAViz: A Browser-based Visualization Tool for Computational Pathology Models |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2402.09990 |