Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
Biomedical Imaging Group Rotterdam, Departments Radiology and Medical Informatics, Erasmus Medical Center, Rotterdam, the Netherlands; The Image Section, Department Computer Science, University of Copenhagen, Copenhagen, Denmark.
Med Image Anal. 2019 May;54:280-296. doi: 10.1016/j.media.2019.03.009. Epub 2019 Mar 29.
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.
机器学习（ML）算法在医学成像领域产生了巨大的影响。虽然医学成像数据集的规模一直在增长，但监督 ML 算法经常面临的一个挑战是缺乏标注数据。因此，已经提出了各种可以使用较少/其他类型的监督进行学习的方法。我们综述了医学成像中的半监督、多实例和迁移学习，包括诊断或分割任务。我们还讨论了这些学习场景之间的联系，以及未来研究的机会。通过 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 可以获得调查文献的详细信息数据集。
