Attribute Relation Modeling for Pulmonary Nodule Malignancy Reasoning

Abstract

Predicting the malignancy of pulmonary nodules found in chest CT images have become much more accurate due to powerful deep convolutional neural networks. However, attributes, such as lobulation, spiculation, and texture, as well as the correlations and dependencies among such attributes have rarely been exploited in deep learning-based algorithms albeit they are frequently used by human experts during nodule assessment. In this paper, we propose a hybrid machine learning framework consisting of two relation modeling modules: Attribute Graph Network and Bayesian Network, which effectively take advantage of attributes and the correlations and dependencies among them to improve the classification performance of pulmonary nodules. According to experiments on the LIDC−IDRI benchmark dataset, our method achieves an accuracy of 93.59%, which gains a 4.57% improvement over the 3D Dense-FPN baseline.