  Thoracic diseases are very serious health problems that plague a large number
of people. Chest X-ray is currently one of the most popular methods to diagnose
thoracic diseases, playing an important role in the healthcare workflow.
However, reading the chest X-ray images and giving an accurate diagnosis remain
challenging tasks for expert radiologists. With the success of deep learning in
computer vision, a growing number of deep neural network architectures were
applied to chest X-ray image classification. However, most of the previous deep
neural network classifiers were based on deterministic architectures which are
usually very noise-sensitive and are likely to aggravate the overfitting issue.
In this paper, to make a deep architecture more robust to noise and to reduce
overfitting, we propose using deep generative classifiers to automatically
diagnose thorax diseases from the chest X-ray images. Unlike the traditional
deterministic classifier, a deep generative classifier has a distribution
middle layer in the deep neural network. A sampling layer then draws a random
sample from the distribution layer and input it to the following layer for
classification. The classifier is generative because the class label is
generated from samples of a related distribution. Through training the model
with a certain amount of randomness, the deep generative classifiers are
expected to be robust to noise and can reduce overfitting and then achieve good
performances. We implemented our deep generative classifiers based on a number
of well-known deterministic neural network architectures, and tested our models
on the chest X-ray14 dataset. The results demonstrated the superiority of deep
generative classifiers compared with the corresponding deep deterministic
classifiers.
