Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
This is an incremental survey paper that reviews existing methods for medical imaging researchers and practitioners dealing with data annotation issues.
The paper surveys semi-supervised, multi-instance, and transfer learning methods in medical image analysis to address the challenge of limited annotated data, highlighting their applications in diagnosis, detection, and segmentation tasks.
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.