  Background and Objective:Computer-aided diagnosis (CAD) systems promote
diagnosis effectiveness and alleviate pressure of radiologists. A CAD system
for lung cancer diagnosis includes nodule candidate detection and nodule
malignancy evaluation. Recently, deep learning-based pulmonary nodule detection
has reached satisfactory performance ready for clinical application. However,
deep learning-based nodule malignancy evaluation depends on heuristic inference
from low-dose computed tomography volume to malignant probability, which lacks
clinical cognition. Methods:In this paper, we propose a joint radiology
analysis and malignancy evaluation network (R2MNet) to evaluate the pulmonary
nodule malignancy via radiology characteristics analysis. Radiological features
are extracted as channel descriptor to highlight specific regions of the input
volume that are critical for nodule malignancy evaluation. In addition, for
model explanations, we propose channel-dependent activation mapping to
visualize the features and shed light on the decision process of deep neural
network. Results:Experimental results on the LIDC-IDRI dataset demonstrate that
the proposed method achieved area under curve of 96.27% on nodule radiology
analysis and AUC of 97.52% on nodule malignancy evaluation. In addition,
explanations of CDAM features proved that the shape and density of nodule
regions were two critical factors that influence a nodule to be inferred as
malignant, which conforms with the diagnosis cognition of experienced
radiologists. Conclusion:Incorporating radiology analysis with nodule malignant
evaluation, the network inference process conforms to the diagnostic procedure
of radiologists and increases the confidence of evaluation results. Besides,
model interpretation with CDAM features shed light on the regions which DNNs
focus on when they estimate nodule malignancy probabilities.
