Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis.

Journal: Clinical and translational medicine
Published Date: Jul 1, 2023

Abstract

INTRODUCTION: Differentiation of histologically similar structures in the liver, including anatomical structures, benign bile duct lesions, or common types of liver metastases, can be challenging with conventional histological tissue sections alone. Accurate histopathological classification is paramount for the diagnosis and adequate treatment of the disease. Deep learning algorithms have been proposed for objective and consistent assessment of digital histopathological images.

Authors

  • Mark Kriegsmann
    Department of General Pathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany.
  • Katharina Kriegsmann
    Department of Hematology, Oncology and Rheumatology, Heidelberg University, Heidelberg, Germany.
  • Georg Steinbuss
    Department of Hematology, Oncology and Rheumatology, Heidelberg University, Heidelberg, Germany.
  • Christiane Zgorzelski
    Institute of Pathology, Heidelberg University, Heidelberg, Germany.
  • Thomas Albrecht
    Institute of Pathology, Heidelberg University, Heidelberg, Germany.
  • Stefan Heinrich
    Department of General, Visceral, and Transplantation Surgery, University Hospital of Mainz, Germany.
  • Stefan Farkas
    Department of Surgery, St. Josefs- Hospital, Wiesbaden, Germany.
  • Wilfried Roth
    Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
  • Hien Dang
    Department of Computer Science, University of Massachusetts Boston, Boston, MA, USA. hiendt@tlu.edu.vn.
  • Anne Hausen
    Institute of Pathology, JGU-Mainz, University Medical Center Mainz, Mainz, Germany.
  • Matthias M Gaida
    Institute of Pathology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany.

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