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Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis.
- Source :
-
Medical Image Analysis . May2019, Vol. 54, p280-296. 17p. - Publication Year :
- 2019
-
Abstract
- • 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. 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13618415
- Volume :
- 54
- Database :
- Academic Search Index
- Journal :
- Medical Image Analysis
- Publication Type :
- Academic Journal
- Accession number :
- 136017532
- Full Text :
- https://doi.org/10.1016/j.media.2019.03.009