Advancements in clinical treatment and research are limited by supervised learning techniques that rely on large amounts of annotated data, an expensive task requiring many hours of clinical specialists' time. In this paper, we propose using self-supervised and semi-supervised learning. These techniques perform an auxiliary task that is label-free, scaling up machine-supervision is easier compared with fully-supervised techniques. This paper proposes S4MI (Self-Supervision and Semi-Supervision for Medical Imaging), our pipeline to leverage advances in self and semi-supervision learning. We benchmark them on three medical imaging datasets to analyze their efficacy for classification and segmentation. This advancement in self-supervised learning with 10% annotation performed better than 100% annotation for the classification of most datasets. The semi-supervised approach yielded favorable outcomes for segmentation, outperforming the fully-supervised approach by using 50% fewer labels in all three datasets.

翻译：临床治疗与研究的进步受限于依赖大量标注数据的监督学习技术，而标注数据是一项需要临床专家耗费大量时间的昂贵任务。本文提出利用自监督学习和半监督学习技术。这些技术通过执行无需标签的辅助任务，使得机器监督的规模化相较于全监督技术更为简便。本文提出S4MI（医学影像的自监督与半监督方法）流程，旨在利用自监督和半监督学习的最新进展。我们在三个医学影像数据集上对其分类和分割性能进行基准测试。在大多数数据集的分类任务中，仅使用10%标注的自监督学习表现优于使用100%标注的全监督方法。半监督方法在分割任务中取得了优异效果，在全部三个数据集上使用比全监督方法少50%的标签即实现了更优性能。