Lung nodule malignancy prediction in chest CT scans based on a CNN model with auxiliary task learning

Xiaomeng Gu; Fucai Chen; Weiyang Xie; Jun Zhao; Qiang Li

doi:10.1117/12.2581215

15 February 2021 Lung nodule malignancy prediction in chest CT scans based on a CNN model with auxiliary task learning

Xiaomeng Gu, Fucai Chen, Weiyang Xie, Jun Zhao, Qiang Li

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115970S (2021) https://doi.org/10.1117/12.2581215
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Xiaomeng Gu, Fucai Chen, Weiyang Xie, Jun Zhao, and Qiang Li "Lung nodule malignancy prediction in chest CT scans based on a CNN model with auxiliary task learning", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115970S (15 February 2021); https://doi.org/10.1117/12.2581215

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