SliDL: A toolbox for processing whole-slide images in deep learning.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2023
2023
Historique:
received:
27
03
2023
accepted:
20
07
2023
medline:
9
8
2023
pubmed:
7
8
2023
entrez:
7
8
2023
Statut:
epublish
Résumé
The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these tasks, and have the potential to substantially reduce the workload of pathologists. However, WSIs present a number of unique challenges for analysis, requiring special consideration of image annotations, slide and image artefacts, and evaluation of WSI-trained model performance. Here we introduce SliDL, a Python library for performing pre- and post-processing of WSIs. SliDL makes WSI data handling easy, allowing users to perform essential processing tasks in a few simple lines of code, bridging the gap between standard image analysis and WSI analysis. We introduce each of the main functionalities within SliDL: from annotation and tile extraction to tissue detection and model evaluation. We also provide 'code snippets' to guide the user in running SliDL. SliDL has been designed to interact with PyTorch, one of the most widely used deep learning libraries, allowing seamless integration into deep learning workflows. By providing a framework in which deep learning methods for WSI analysis can be developed and applied, SliDL aims to increase the accessibility of an important application of deep learning.
Identifiants
pubmed: 37549131
doi: 10.1371/journal.pone.0289499
pii: PONE-D-23-08782
pmc: PMC10406329
doi:
Substances chimiques
Coloring Agents
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0289499Subventions
Organisme : Cancer Research UK
ID : C14303/A17197
Pays : United Kingdom
Informations de copyright
Copyright: © 2023 Berman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Déclaration de conflit d'intérêts
I have read the journal’s policy and the authors of this manuscript have the following competing interests: M.G. is an employee and shareholder of Cyted Ltd. F.M. is a co-founder and director of Tailor Bio. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
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