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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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| Publicado no: | Eur Radiol |
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| Principais autores: | , , , , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2019
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| Assuntos: | |
| Acesso em linha: | 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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