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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...

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Dades bibliogràfiques
Publicat a:	Eur Radiol
Autors principals:	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:	Artigo
Idioma:	Inglês
Publicat:	2019
Matèries:	Article
Accés en línia:	https://ncbi.nlm.nih.gov/pmc/articles/PMC7243989/ https://ncbi.nlm.nih.gov/pubmed/31093705 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s00330-019-06214-8
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Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features

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