Deep Features for Tissue-Fold Detection in Histopathology Images

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Whole slide imaging (WSI) refers to the digitization of a tissue specimen which enables pathologists to explore high-resolution images on a monitor rather than through a microscope. The formation of tissue folds occur during tissue processing. Their presence may not only cause out-of-focus digitization but can also negatively affect the diagnosis in some cases. In this paper, we have compared five pre-trained convolutional neural networks (CNNs) of different depths as feature extractors to characterize tissue folds. We have also explored common classifiers to discriminate folded tissue against the normal tissue in hematoxylin and eosin (H&E) stained biopsy samples. In our experiments, we manually select the folded area in roughly 2.5 mm 2.5 mm patches at 20x magnification level as the training data. The “DenseNet” with 201 layers alongside an SVM classifier outperformed all other configurations. Based on the leave-one-out validation strategy, we achieved accuracy, whereas with augmentation the accuracy increased to We have tested the generalization of our method with five unseen WSIs from the NIH (National Cancer Institute) dataset. The accuracy for patch-wise detection was One folded patch within an image suffices to flag the entire specimen for visual inspection.

Original languageEnglish (US)
Title of host publicationDigital Pathology - 15th European Congress, ECDP 2019, Proceedings
EditorsConstantino Carlos Reyes-Aldasoro, Andrew Janowczyk, Mitko Veta, Peter Bankhead, Korsuk Sirinukunwattana
PublisherSpringer Verlag
Pages125-132
Number of pages8
ISBN (Print)9783030239367
DOIs
StatePublished - 2019
Event15th European Congress on Digital Pathology, ECDP 2019 - Warwick, United Kingdom
Duration: Apr 10 2019Apr 13 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11435 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th European Congress on Digital Pathology, ECDP 2019
Country/TerritoryUnited Kingdom
CityWarwick
Period4/10/194/13/19

Keywords

  • Deep features
  • Digital pathology
  • SVM
  • Tissue folds

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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