  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.
