  Refer to the literature of lung nodule classification, many studies adopt
Convolutional Neural Networks (CNN) to directly predict the malignancy of lung
nodules with original thoracic Computed Tomography (CT) and nodule location.
However, these studies cannot tell how the CNN works in terms of predicting the
malignancy of the given nodule, e.g., it's hard to conclude that whether the
region within the nodule or the contextual information matters according to the
output of the CNN. In this paper, we propose an interpretable and multi-task
learning CNN -- Joint learning for \textbf{P}ulmonary \textbf{N}odule
\textbf{S}egmentation \textbf{A}ttributes and \textbf{M}alignancy
\textbf{P}rediction (PN-SAMP). It is able to not only accurately predict the
malignancy of lung nodules, but also provide semantic high-level attributes as
well as the areas of detected nodules. Moreover, the combination of nodule
segmentation, attributes and malignancy prediction is helpful to improve the
performance of each single task. In addition, inspired by the fact that
radiologists often change window widths and window centers to help to make
decision on uncertain nodules, PN-SAMP mixes multiple WW/WC together to gain
information for the raw CT input images. To verify the effectiveness of the
proposed method, the evaluation is implemented on the public LIDC-IDRI dataset,
which is one of the largest dataset for lung nodule malignancy prediction.
Experiments indicate that the proposed PN-SAMP achieves significant improvement
with respect to lung nodule classification, and promising performance on lung
nodule segmentation and attribute learning, compared with the-state-of-the-art
methods.
