  Interstitial lung diseases are a large group of heterogeneous diseases
characterized by different degrees of alveolitis and pulmonary fibrosis.
Accurately diagnosing these diseases has significant guiding value for
formulating treatment plans. Although previous work has produced impressive
results in classifying interstitial lung diseases, there is still room for
improving the accuracy of these techniques, mainly to enhance automated
decision-making. In order to improve the classification precision, our study
proposes a convolutional neural networks-based framework with auxiliary
information. Firstly, ILD images are added with their medical information by
re-scaling the original image in Hounsfield Units. Secondly, a modified CNN
model is used to produce a vector of classification probability for each
tissue. Thirdly, location information of the input image, consisting of the
occurrence frequencies of different diseases in the CT scans on certain
locations, is used to calculate a location weight vector. Finally, the Hadamard
product between two vectors is used to produce a decision vector for the
prediction. Compared to the state-of-the-art methods, the results using a
publicly available ILD database show the potential of predicting these using
different auxiliary information.
