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arXiv · 1804.06353

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

Veronika Cheplygina·Marleen de Bruijne·Josien P. W. Pluim
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Abstract

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 which can learn with less/other types of supervision, have been proposed. We review semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis/detection or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research.

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Veronika Cheplygina, Marleen de Bruijne, Josien P. W. Pluim. 2018-04-17. Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. https://arxiv.org/abs/1804.06353

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