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...
| Publicado en: | European Radiology Vol. 29; no. 7; pp. 3348 - 3358 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2019
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| Acceso en línea: | Ver este registro en EBSCOhost |