  Capsule Networks (CapsNets) have demonstrated to be a promising alternative
to Convolutional Neural Networks (CNNs). However, they often fall short of
state-of-the-art accuracies on large-scale high-dimensional datasets. We
propose a Detail-Oriented Capsule Network (DECAPS) that combines the strength
of CapsNets with several novel techniques to boost its classification
accuracies. First, DECAPS uses an Inverted Dynamic Routing (IDR) mechanism to
group lower-level capsules into heads before sending them to higher-level
capsules. This strategy enables capsules to selectively attend to small but
informative details within the data which may be lost during pooling operations
in CNNs. Second, DECAPS employs a Peekaboo training procedure, which encourages
the network to focus on fine-grained information through a second-level
attention scheme. Finally, the distillation process improves the robustness of
DECAPS by averaging over the original and attended image region predictions. We
provide extensive experiments on the CheXpert and RSNA Pneumonia datasets to
validate the effectiveness of DECAPS. Our networks achieve state-of-the-art
accuracies not only in classification (increasing the average area under ROC
curves from 87.24% to 92.82% on the CheXpert dataset) but also in the
weakly-supervised localization of diseased areas (increasing average precision
from 41.7% to 80% for the RSNA Pneumonia detection dataset).
