Transfer learning for medical image classification: a literature review.

Journal: BMC medical imaging
Published Date: Apr 13, 2022

Abstract

BACKGROUND: Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and hardware resources. However, transfer learning has been arbitrarily configured in the majority of studies. This review paper attempts to provide guidance for selecting a model and TL approaches for the medical image classification task.

Authors

  • Hee E Kim
    Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany. HeeEun.Kim@medma.uni-heidelberg.de.
  • Alejandro Cosa-Linan
    Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
  • Nandhini Santhanam
    Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
  • Mahboubeh Jannesari
    Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
  • Máté E Maros
    Institute of Medical Biometry and Informatics (IMBI), University of Heidelberg, Heidelberg, Germany.
  • Thomas Ganslandt
    Center for Medical Information and Communication, Erlangen University Hospital, Erlangen, Germany.

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