  We present a semi-supervised algorithm for lung cancer screening in which a
3D Convolutional Neural Network (CNN) is trained using the
Expectation-Maximization (EM) meta-algorithm. Semi-supervised learning allows a
smaller labelled data-set to be combined with an unlabeled data-set in order to
provide a larger and more diverse training sample. EM allows the algorithm to
simultaneously calculate a maximum likelihood estimate of the CNN training
coefficients along with the labels for the unlabeled training set which are
defined as a latent variable space. We evaluate the model performance of the
Semi-Supervised EM algorithm for CNNs through cross-domain training of the
Kaggle Data Science Bowl 2017 (Kaggle17) data-set with the National Lung
Screening Trial (NLST) data-set. Our results show that the Semi-Supervised EM
algorithm greatly improves the classification accuracy of the cross-domain lung
cancer screening, although results are lower than a fully supervised approach
with the advantage of additional labelled data from the unsupervised sample. As
such, we demonstrate that Semi-Supervised EM is a valuable technique to improve
the accuracy of lung cancer screening models using 3D CNNs.
