Whole slide image understanding in pathology: what is the salient scale of analysis?

Eleanor Jenkinson, Oggie Arandelovic*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Background: In recent years, there has been increasing research in the applications of Artificial Intelligence in the medical industry. Digital pathology has seen great success in introducing the use of technology in the digitisation and analysis of pathology slides to ease the burden of work on pathologists. Digitised pathology slides, otherwise known as whole slide images, can be analysed by pathologists with the same methods used to analyse traditional glass slides. Methods: The digitisation of pathology slides has also led to the possibility of using these whole slide images to train machine learning models to detect tumours. Patch-based methods are common in the analysis of whole slide images as these images are too large to be processed using normal machine learning methods. However, there is little work exploring the effect that the size of the patches has on the analysis. A patch-based whole slide image analysis method was implemented and then used to evaluate and compare the accuracy of the analysis using patches of different sizes. In addition, two different patch sampling methods are used to test if the optimal patch size is the same for both methods, as well as a downsampling method where whole slide images of low resolution images are used to train an analysis model. Results: It was discovered that the most successful method uses a patch size of 256 × 256 pixels with the informed sampling method, using the location of tumour regions to sample a balanced dataset. Conclusion: Future work on batch-based analysis of whole slide images in pathology should take into account our findings when designing new models.
Original languageEnglish
Pages (from-to)489-518
Number of pages30
JournalBioMedInformatics
Volume4
Issue number1
DOIs
Publication statusPublished - 14 Feb 2024

Keywords

  • WSI
  • Patches
  • Tumour
  • Cancer
  • Deep learning
  • Camelyon17

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