An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification

作者：

Highlights：

• A novel interpretable hierarchical deep learning model for lung cancer diagnosis.

• Network design incorporates semantic features that are intuitive to radiologists.

• A single joint network predicts interpretable features and malignancy.

• Architecture maintains prediction accuracy while improving model interpretability.

摘要

•A novel interpretable hierarchical deep learning model for lung cancer diagnosis.•Network design incorporates semantic features that are intuitive to radiologists.•A single joint network predicts interpretable features and malignancy.•Architecture maintains prediction accuracy while improving model interpretability.

论文关键词：Lung nodule classification,Lung cancer diagnosis,Computed tomography,Deep learning,Convolutional neural networks,Model interpretability

论文评审过程：Received 3 June 2018, Revised 8 December 2018, Accepted 15 January 2019, Available online 18 January 2019, Version of Record 25 March 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.01.048