Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Veronika Cheplygina (Corresponding author), Marleen de Bruijne, Josien P.W. Pluim

Research output: Contribution to journalArticleAcademicpeer-review

79 Citations (Scopus)
3 Downloads (Pure)

Abstract

Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods that can learn with less/other types of supervision, have been proposed. We give an overview of semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research. A dataset with the details of the surveyed papers is available via https://figshare.com/articles/Database_of_surveyed_literature_in_Not-so-supervised_a_survey_of_semi-supervised_multi-instance_and_transfer_learning_in_medical_image_analysis_/7479416.

Original languageEnglish
Pages (from-to)280-296
Number of pages17
JournalMedical Image Analysis
Volume54
DOIs
Publication statusPublished - May 2019

Keywords

  • Computer aided diagnosis
  • Machine learning
  • Medical imaging
  • Multi-task learning
  • Multiple instance learning
  • Semi-supervised learning
  • Transfer learning
  • Weakly-supervised learning
  • Diagnostic Imaging
  • Humans
  • Image Processing, Computer-Assisted/methods
  • Supervised Machine Learning

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