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

•We discuss different forms of supervision in medical image analysis.•Over 140 papers using semi-supervised, multi-instance or transfer learning are covered.•We discuss connections between these scenarios and further opportunities for research. [Display omitted] Machine learning (ML) algorithms have...

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Vydáno v:	Medical image analysis Ročník 54; s. 280 - 296
Hlavní autoři:	Cheplygina, Veronika, de Bruijne, Marleen, Pluim, Josien P.W.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier B.V 01.05.2019 Elsevier BV
Témata:	Algorithms Computer aided diagnosis Datasets Image analysis Image processing Image segmentation Learning algorithms Machine learning Medical imaging Multi-task learning Multiple instance learning Polls & surveys Semi-supervised learning Transfer learning Weakly-supervised learning Medical imaging Weakly-supervised learning Semi-supervised learning Transfer learning Machine learning Computer aided diagnosis Multiple instance learning Multi-task learning
ISSN:	1361-8415, 1361-8423, 1361-8423
On-line přístup:	Získat plný text
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Shrnutí:	•We discuss different forms of supervision in medical image analysis.•Over 140 papers using semi-supervised, multi-instance or transfer learning are covered.•We discuss connections between these scenarios and further opportunities for research. [Display omitted] 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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23
ISSN:	1361-8415 1361-8423 1361-8423
DOI:	10.1016/j.media.2019.03.009