Cargando…
Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection
Recent advancements in deep learning have led to a resurgence of medical imaging and Electronic Medical Record (EMR) models for a variety of applications, including clinical decision support, automated workflow triage, clinical prediction and more. However, very few models have been developed to int...
Autores principales: | Huang, Shih-Cheng, Pareek, Anuj, Zamanian, Roham, Banerjee, Imon, Lungren, Matthew P. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746687/ https://www.ncbi.nlm.nih.gov/pubmed/33335111 http://dx.doi.org/10.1038/s41598-020-78888-w |
Ejemplares similares
-
Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines
por: Huang, Shih-Cheng, et al.
Publicado: (2020) -
PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging
por: Huang, Shih-Cheng, et al.
Publicado: (2020) -
Author Correction: PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging
por: Huang, Shih-Cheng, et al.
Publicado: (2020) -
Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support
por: Banerjee, Imon, et al.
Publicado: (2019) -
Deep learning and its role in COVID-19 medical imaging
por: Desai, Sudhen B., et al.
Publicado: (2020)