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

2018/04/17 by Veronika Cheplygina, Marleen de Bruijne, Cheplygina, Veronika +3 · 20 citations
Computer Science · Medicine · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Image Segmentation Techniques #Radiomics and Machine Learning in Medical Imaging #cs.CV

paper · pdf · doi:10.48550/arxiv.1804.06353

Submitted to Medical Image Analysis

openalex publication_date 2018/04/17 · arxiv created 2018/09/14 · arxiv updated 2018/09/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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