6/3/2023 0 Comments Easy reports zoomifyFor reproducibility of algorithms, we provide pretrained published benchmark algorithms which can be run using only a few lines of code. It hides unnecessary details of various file formats while keeping intact important format-related metadata required for ML tasks. The WSI reading capability of the toolbox is a good example of such abstraction that simplifies WSI reading. This means that the API users can write code with a focus on the task at hand instead of being distracted by unnecessary details or peripheral tasks, such as managing multiple processes or needing to know the details of different WSI formats. To achieve this, we provide a simple to use Application Programming Interface (API) which abstracts unnecessary complexity from the user where possible. Our main objective is to provide an open-source library to the CPath community, which is simplified, streamlined, reproducible, easy to use, unit-tested and allows researchers to build their analytical pipelines on state-of-the-art methods. TIAToolbox is a suite of unit-tested image analysis and machine learning (ML) tools developed for the CPath community, making it possible for a variety of users to construct and reproduce CPath analytical pipelines with cutting-edge methods. This may prevent code from a published peer-reviewed method from being able to run out of the box, decrease the reproducibility of experiments, handicap the ability to extend or adapt existing methods and increase the time required to understand the codebase. It is also common for there to be little to no code quality checks or unit testing. Several published algorithms have their own packaged codebases which run in a task-specific environment, with tightly coupled interfaces, dependencies, and image format requirements. Although many algorithms have been developed for the analysis of WSIs which all share the same basic components (such as WSI reading, patch extraction and feeding to deep neural networks), there is no single open-source generic library that unifies all the steps using best practice to process these images. Such advances have benefited greatly by adapting deep learning techniques from computer vision producing novel solutions to a variety of CPath problems, including nucleus instance segmentation 1, pathology image quality analysis 2 and WSI-level prediction 3, 4. Model weights can be made available for commercial use on request depending on ethical approvals from the data source.ĭigitization of classical cellular pathology workflows through the deployment of digital whole slide image (WSI) scanners has resulted in significant progress in the development of computational pathology (CPath) image analysis techniques. All parts of the toolbox, including model weights, may be freely used for research and non-commercial purposes. Model weights downloaded at runtime are publicly hosted and maintained on TIA Centre servers under a creative commons non-commercial use license (CC-BY-NC 4.0). The private oral dysplasia cohort dataset is not available because we do not currently have ethical approval to share this dataset publicly but the trained model is already published with ethical approval details listed in the original publication 28.Īll source code for TIAToolbox is available on GitHub ( ) and Zenodo 50 (10.5281/zenodo.6808365) under the BSD 3-clause license. They can be accessed for research and non-commercial use at the following web addresses: The Cancer Genome Atlas (TCGA) 47 available at, PanNuke 36, 37 available at, PatchCamelyon (PCam) 30 available at, Kather 100k 29, 48 available at, Kumar (MoNuSeg Subset) 49 available at, MoNuSAC 38 available at and CoNSeP 1 available at. GUID: 259EBEE8-2076-4A34-B8C9-7B532ABE9140 Data Availability StatementĪll datasets analysed during the production of TIAToolbox, except for one private oral dysplasia cohort dataset for HoVer-Net + , are publicly available.
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