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

Veronika Cheplygina, Marleen de Bruijne, Josien P.W. Pluim

Publikation: Artikel i tidsskrift og konference artikel i tidsskriftTidsskriftartikelForskningpeer review

Abstrakt

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.
OriginalsprogEngelsk
TidsskriftMedical Image Analysis
Vol/bind54
Sider (fra-til)280-296
Antal sider17
ISSN1361-8415
DOI
StatusUdgivet - maj 2019
Udgivet eksterntJa

Fingeraftryk

Dyk ned i forskningsemnerne om 'Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis'. Sammen danner de et unikt fingeraftryk.

Citationsformater