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 .
In their survey Cheplygina et al. present a variety of methods in the context of medical image analysis REF .
Not‐so‐supervised: A survey of semi‐supervised, multi‐instance, and transfer learning in medical image analysis
Medical Image Analysis
Computer Science Medicine Mathematics