Implementing deep learning models for the classification of Echinococcus multilocularis infection in human liver tissue.

Journal: Parasites & vectors
PMID: 36694210

Abstract

BACKGROUND: The histological diagnosis of alveolar echinococcosis can be challenging. Decision support models based on deep learning (DL) are increasingly used to aid pathologists, but data on the histology of tissue-invasive parasitic infections are missing. The aim of this study was to implement DL methods to classify Echinococcus multilocularis liver lesions and normal liver tissue and assess which regions and structures play the most important role in classification decisions.

Authors

  • Mihály Sulyok
    Institute for Pathology and Neuropathology, Eberhard Karls University, University Hospital of Tübingen , Tübingen 72076, Germany.
  • Julia Luibrand
    Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
  • Jens Strohäker
    Department of Surgery, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
  • Peter Karacsonyi
    Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
  • Leonie Frauenfeld
    Institute for Pathology and Neuropathology, Eberhard Karls University, University Hospital of Tübingen , Tübingen 72076, Germany.
  • Ahmad Makky
    Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
  • Sven Mattern
    Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
  • Jing Zhao
    Department of Pharmacy, Pharmacoepidemiology and Drug Safety Research Group, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
  • Silvio Nadalin
    Department of Surgery, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
  • Falko Fend
    Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
  • Christian M Schürch
    Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, 94305, USA. christian.schuerch@med.uni-tuebingen.de.

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