论文标题
焦点：3D医学图像检测的半监督学习
FocalMix: Semi-Supervised Learning for 3D Medical Image Detection
论文作者
论文摘要
在医学成像中应用人工智能技术是医学中最有希望的领域之一。但是，该领域最近的大部分成功都高度依赖大量仔细注释的数据，而注释的医学图像是一个昂贵的过程。在本文中，我们提出了一种称为FocalMix的新方法，据我们所知，该方法是第一个利用半监督学习（SSL）的最新进展进行3D医学图像检测。我们在两个广泛使用的数据集上进行了广泛的实验，以检测肺结核，即LUNA16和NLST。结果表明，我们提出的SSL方法可以通过400张未标记的CT扫描来实现高达17.3％的大量改善。
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.
