  Characterization of lung nodules as benign or malignant is one of the most
important tasks in lung cancer diagnosis, staging and treatment planning. While
the variation in the appearance of the nodules remains large, there is a need
for a fast and robust computer aided system. In this work, we propose an
end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for
nodule characterization. First, we use median intensity projection to obtain a
2D patch corresponding to each dimension. The three images are then
concatenated to form a tensor, where the images serve as different channels of
the input image. In order to increase the number of training samples, we
perform data augmentation by scaling, rotating and adding noise to the input
image. The trained network is used to extract features from the input image
followed by a Gaussian Process (GP) regression to obtain the malignancy score.
We also empirically establish the significance of different high level nodule
attributes such as calcification, sphericity and others for malignancy
determination. These attributes are found to be complementary to the deep
multi-view CNN features and a significant improvement over other methods is
obtained.
