  Background and Objective: Early detection of lung cancer is crucial as it has
high mortality rate with patients commonly present with the disease at stage 3
and above. There are only relatively few methods that simultaneously detect and
classify nodules from computed tomography (CT) scans. Furthermore, very few
studies have used semi-supervised learning for lung cancer prediction. This
study presents a complete end-to-end scheme to detect and classify lung nodules
using the state-of-the-art Self-training with Noisy Student method on a
comprehensive CT lung screening dataset of around 4,000 CT scans.
  Methods: We used three datasets, namely LUNA16, LIDC and NLST, for this
study. We first utilise a three-dimensional deep convolutional neural network
model to detect lung nodules in the detection stage. The classification model
known as Maxout Local-Global Network uses non-local networks to detect global
features including shape features, residual blocks to detect local features
including nodule texture, and a Maxout layer to detect nodule variations. We
trained the first Self-training with Noisy Student model to predict lung cancer
on the unlabelled NLST datasets. Then, we performed Mixup regularization to
enhance our scheme and provide robustness to erroneous labels.
  Results and Conclusions: Our new Mixup Maxout Local-Global network achieves
an AUC of 0.87 on 2,005 completely independent testing scans from the NLST
dataset. Our new scheme significantly outperformed the next highest performing
method at the 5% significance level using DeLong's test (p = 0.0001). This
study presents a new complete end-to-end scheme to predict lung cancer using
Self-training with Noisy Student combined with Mixup regularization. On a
completely independent dataset of 2,005 scans, we achieved state-of-the-art
performance even with more images as compared to other methods.
