Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features.

Objectives: To develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier.Methods: A convolutional neural network (CNN) was engineered and trained to classify six hepatic tumor entities using 494 lesions o...

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Bibliographic Details

Published in:	European Radiology Vol. 29; no. 7; pp. 3348 - 3358
Main Authors:	Wang, Clinton J., Hamm, Charlie A., Savic, Lynn J., Ferrante, Marc, Schobert, Isabel, Schlachter, Todd, Lin, MingDe, Weinreb, Jeffrey C., Duncan, James S., Chapiro, Julius, Letzen, Brian
Format:	Journal Article
Published:	Springer Nature Jul2019
Online Access:	View this record in EBSCOhost