  We present and evaluate a new deep neural network architecture for automatic
thoracic disease detection on chest X-rays. Deep neural networks have shown
great success in a plethora of visual recognition tasks such as image
classification and object detection by stacking multiple layers of
convolutional neural networks (CNN) in a feed-forward manner. However, the
performance gain by going deeper has reached bottlenecks as a result of the
trade-off between model complexity and discrimination power. We address this
problem by utilizing the recently developed routing-by agreement mechanism in
our architecture. A novel characteristic of our network structure is that it
extends routing to two types of layer connections (1) connection between
feature maps in dense layers, (2) connection between primary capsules and
prediction capsules in final classification layer. We show that our networks
achieve comparable results with much fewer layers in the measurement of AUC
score. We further show the combined benefits of model interpretability by
generating Gradient-weighted Class Activation Mapping (Grad-CAM) for
localization. We demonstrate our results on the NIH chestX-ray14 dataset that
consists of 112,120 images on 30,805 unique patients including 14 kinds of lung
diseases.
