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Deep convolutional neural networks in the classification of dual-energy thoracic radiographic views for efficient workflow: analysis on over 6500 clinical radiographs
DICOM header information is frequently used to classify medical image types; however, if a header is missing fields or contains incorrect data, the utility is limited. To expedite image classification, we trained convolutional neural networks (CNNs) in two classification tasks for thoracic radiograp...
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| Publicado no: | J Med Imaging (Bellingham) |
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| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Society of Photo-Optical Instrumentation Engineers
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6995870/ https://ncbi.nlm.nih.gov/pubmed/32042858 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1117/1.JMI.7.1.016501 |
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