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

2018/04/17 by Veronika Cheplygina, Marleen de Bruijne, Cheplygina, Veronika +3 · 9 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

paper · pdf · doi:10.48550/arxiv.1804.06353

openalex publication_date 2018/04/17 · 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\nof medical imaging. While medical imaging datasets have been growing in size, a\nchallenge for supervised ML algorithms that is frequently mentioned is the lack\nof annotated data. As a result, various methods which can learn with less/other\ntypes of supervision, have been proposed. We review semi-supervised, multiple\ninstance, and transfer learning in medical imaging, both in diagnosis/detection\nor segmentation tasks. We also discuss connections between these learning\nscenarios, and opportunities for future research.\n

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