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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| Veröffentlicht in: | Medical image analysis Jg. 54; S. 280 - 296 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Netherlands
Elsevier B.V
01.05.2019
Elsevier BV |
| Schlagworte: | |
| ISSN: | 1361-8415, 1361-8423, 1361-8423 |
| Online-Zugang: | Volltext |
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| 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.
[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. |
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| AbstractList | 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.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. 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. •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. |
| Author | Cheplygina, Veronika Pluim, Josien P.W. de Bruijne, Marleen |
| Author_xml | – sequence: 1 givenname: Veronika orcidid: 0000-0003-0176-9324 surname: Cheplygina fullname: Cheplygina, Veronika email: v.cheplygina@tue.nl organization: Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands – sequence: 2 givenname: Marleen orcidid: 0000-0002-6328-902X surname: de Bruijne fullname: de Bruijne, Marleen organization: Biomedical Imaging Group Rotterdam, Departments Radiology and Medical Informatics, Erasmus Medical Center, Rotterdam, the Netherlands – sequence: 3 givenname: Josien P.W. surname: Pluim fullname: Pluim, Josien P.W. organization: Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30959445$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 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 |
| Title | Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis |
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