  Early detection of lung nodules with computed tomography (CT) is critical for
the longer survival of lung cancer patients and better quality of life.
Computer-aided detection/diagnosis (CAD) is proven valuable as a second or
concurrent reader in this context. However, accurate detection of lung nodules
remains a challenge for such CAD systems and even radiologists due to not only
the variability in size, location, and appearance of lung nodules but also the
complexity of lung structures. This leads to a high false-positive rate with
CAD, compromising its clinical efficacy. Motivated by recent computer vision
techniques, here we present a self-supervised region-based 3D transformer model
to identify lung nodules among a set of candidate regions. Specifically, a 3D
vision transformer (ViT) is developed that divides a CT image volume into a
sequence of non-overlap cubes, extracts embedding features from each cube with
an embedding layer, and analyzes all embedding features with a self-attention
mechanism for the prediction. To effectively train the transformer model on a
relatively small dataset, the region-based contrastive learning method is used
to boost the performance by pre-training the 3D transformer with public CT
images. Our experiments show that the proposed method can significantly improve
the performance of lung nodule screening in comparison with the commonly used
3D convolutional neural networks.
