Cargando…
Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines
Advancements in deep learning techniques carry the potential to make significant contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis, prognosis, and treatment decisions. The current state-of-the-art deep learning models for radiology applications consider o...
Autores principales: | Huang, Shih-Cheng, Pareek, Anuj, Seyyedi, Saeed, 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/PMC7567861/ https://www.ncbi.nlm.nih.gov/pubmed/33083571 http://dx.doi.org/10.1038/s41746-020-00341-z |
Ejemplares similares
-
Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection
por: Huang, Shih-Cheng, et al.
Publicado: (2020) -
Self-supervised learning for medical image classification: a systematic review and implementation guidelines
por: Huang, Shih-Cheng, et al.
Publicado: (2023) -
Deep learning and its role in COVID-19 medical imaging
por: Desai, Sudhen B., et al.
Publicado: (2020) -
Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset
por: Sadasivuni, Sudarsan, et al.
Publicado: (2022) -
PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging
por: Huang, Shih-Cheng, et al.
Publicado: (2020)