  Risk stratification of lung nodules is a task of primary importance in lung
cancer diagnosis. Any improvement in robust and accurate nodule
characterization can assist in identifying cancer stage, prognosis, and
improving treatment planning. In this study, we propose a 3D Convolutional
Neural Network (CNN) based nodule characterization strategy. With a completely
3D approach, we utilize the volumetric information from a CT scan which would
be otherwise lost in the conventional 2D CNN based approaches. In order to
address the need for a large amount for training data for CNN, we resort to
transfer learning to obtain highly discriminative features. Moreover, we also
acquire the task dependent feature representation for six high-level nodule
attributes and fuse this complementary information via a Multi-task learning
(MTL) framework. Finally, we propose to incorporate potential disagreement
among radiologists while scoring different nodule attributes in a graph
regularized sparse multi-task learning. We evaluated our proposed approach on
one of the largest publicly available lung nodule datasets comprising 1018
scans and obtained state-of-the-art results in regressing the malignancy
scores.
