Recent studies have shown that lung cancer screening using annual low-dose
computed tomography (CT) reduces lung cancer mortality by 20% compared to
traditional chest radiography. Therefore, CT lung screening has started to be
used widely all across the world. However, analyzing these images is a serious
burden for radiologists. The number of slices in a CT scan can be up to 600.
Therefore, computer-aided-detection (CAD) systems are very important for faster
and more accurate assessment of the data. In this study, we proposed a
framework that analyzes CT lung screenings using convolutional neural networks
(CNNs) to reduce false positives. We trained our model with different volume
sizes and showed that volume size plays a critical role in the performance of
the system. We also used different fusions in order to show their power and
effect on the overall accuracy. 3D CNNs were preferred over 2D CNNs because 2D
convolutional operations applied to 3D data could result in information loss.
The proposed framework has been tested on the dataset provided by the LUNA16
Challenge and resulted in a sensitivity of 0.831 at 1 false positive per scan.

使用卷积神经网络分析低剂量 CT 肺癌筛查图像以减少错误结果，并在 LUNA16 挑战数据集上表现出 82.1% 的敏感性。

卷积神经网络在肺部结节分类中输入大小的影响

Effect of Input Size on the Classification of Lung Nodules Using  Convolutional Neural Networks

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.

提出一种基于半监督学习和 3D 卷积神经网络的肺癌筛查算法，采用 Expectation-Maximization 元算法进行训练，通过交叉域的方法验证其模型性能，结果表明该算法能够极大地提高肺癌筛查的分类准确性，证明了半监督 EM 是改进 3D CNN 肺癌筛查模型准确性的一种有效技术。