Convolutional neural networks (CNNs) have been increasingly applied to computer-aided diagnosis (CADx) for lung nodule malignancy prediction, which usually is a binary classification task. However, CNNs were often difficult to capture optimal features, thereby affect the classification performance. This study developed a CADx system based on a CNN model with auxiliary task learning to predict lung nodule malignancy in chest computed tomography (CT) scans. Our CADx system took raw CT image cubes centering at nodules as input and generated one main output and eight auxiliary outputs. The main output predicted lung nodule malignancy; the auxiliary outputs predicted lesion size and characteristics. The auxiliary tasks offered assistance for predicting the final nodule malignancy. The performance of the developed lung nodule CADx system was verified by use of the LIDC dataset. Results showed that our CADx system achieved improved performance for lung nodule malignancy prediction.
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