Deep learning for whole slide image analysis: an overview

Neofytos Dimitriou, Ognjen Arandjelović, Peter D. Caie

Research output: Contribution to journalReview articlepeer-review

184 Citations (Scopus)
1 Downloads (Pure)


The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artifacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.
Original languageEnglish
Article number264
Number of pages7
JournalFrontiers in Medicine
Publication statusPublished - 22 Nov 2019


  • Digital pathology
  • Computer vision
  • Oncology
  • Cancer
  • Machine learning
  • Personalized pathology
  • Image analysis


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