  Accurate characterisation of visual attributes such as spiculation,
lobulation, and calcification of lung nodules is critical in cancer management.
The characterisation of these attributes is often subjective, which may lead to
high inter- and intra-observer variability. Furthermore, lung nodules are often
heterogeneous in the cross-sectional image slices of a 3D volume. Current
state-of-the-art methods that score multiple attributes rely on deep
learning-based multi-task learning (MTL) schemes. These methods, however,
extract shared visual features across attributes and then examine each
attribute without explicitly leveraging their inherent intercorrelations.
Furthermore, current methods either treat each slice with equal importance
without considering their relevance or heterogeneity, or restrict the number of
input slices, which limits performance. In this study, we address these
challenges with a new convolutional neural network (CNN)-based MTL model that
incorporates attention modules to simultaneously score 9 visual attributes of
lung nodules in computed tomography (CT) image volumes. Our model processes
entire nodule volumes of arbitrary depth and uses a slice attention module to
filter out irrelevant slices. We also introduce cross-attribute and attribute
specialisation attention modules that learn an optimal amalgamation of
meaningful representations to leverage relationships between attributes. We
demonstrate that our model outperforms previous state-of-the-art methods at
scoring attributes using the well-known public LIDC-IDRI dataset of pulmonary
nodules from over 1,000 patients. Our attention modules also provide
easy-to-interpret weights that offer insights into the predictions of the
model.
