Applying artificial intelligence techniques in medical imaging is one of the most promising areas in medicine. However, most of the recent success in this area highly relies on large amounts of carefully annotated data, whereas annotating medical images is a costly process. In this paper, we propose a novel method, called FocalMix, which, to the best of our knowledge, is the first to leverage recent advances in semi-supervised learning (SSL) for 3D medical image detection. We conducted extensive experiments on two widely used datasets for lung nodule detection, LUNA16 and NLST. Results show that our proposed SSL methods can achieve a substantial improvement of up to 17.3% over state-of-the-art supervised learning approaches with 400 unlabeled CT scans.
翻译：在医学成像中应用人工智能技术是医学领域最有希望的领域之一,然而,该领域最近取得的大部分成功高度依赖于大量经过仔细注解的数据,而医疗图象的注解是一个昂贵的过程。 在本文中,我们提出了一个名为CouncleMix的新方法,据我们所知,这是利用半监督学习(SSL)的最新进展进行3D医学成像检测的第一个新方法。 我们对两种广泛使用的用于肺结核检测的数据集(LUNA16和NLST)进行了广泛的实验。 结果显示,我们提议的SSL方法可以大大改进,比最先进的监控学习方法高出17.3%,有400个未贴标签的CT扫描。