Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• Medical image analysis, 2019-05, Vol.54, p.280-296

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• •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.