  We introduce a new computer aided detection and diagnosis system for lung
cancer screening with low-dose CT scans that produces meaningful probability
assessments. Our system is based entirely on 3D convolutional neural networks
and achieves state-of-the-art performance for both lung nodule detection and
malignancy classification tasks on the publicly available LUNA16 and Kaggle
Data Science Bowl challenges. While nodule detection systems are typically
designed and optimized on their own, we find that it is important to consider
the coupling between detection and diagnosis components. Exploiting this
coupling allows us to develop an end-to-end system that has higher and more
robust performance and eliminates the need for a nodule detection false
positive reduction stage. Furthermore, we characterize model uncertainty in our
deep learning systems, a first for lung CT analysis, and show that we can use
this to provide well-calibrated classification probabilities for both nodule
detection and patient malignancy diagnosis. These calibrated probabilities
informed by model uncertainty can be used for subsequent risk-based decision
making towards diagnostic interventions or disease treatments, as we
demonstrate using a probability-based patient referral strategy to further
improve our results.
