Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Research output: Contribution to journal › Journal article › Research › peer-review
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 language | English |
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Journal | Medical Image Analysis |
Volume | 54 |
Pages (from-to) | 280-296 |
Number of pages | 17 |
ISSN | 1361-8415 |
DOIs | |
Publication status | Published - 29 Mar 2019 |
Bibliographical note
Copyright © 2019. Published by Elsevier B.V.
Links
- https://arxiv.org/pdf/1804.06353
Submitted manuscript
ID: 217120622