Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph
Abstract Image-based teleconsultation using smartphones has become increasingly popular. In parallel, deep learning algorithms have been developed to detect radiological findings in chest X-rays (CXRs). However, the feasibility of using smartphones to automate this process has yet to be evaluated. T...
Guardado en:
Autores principales: | Po-Chih Kuo, Cheng Che Tsai, Diego M. López, Alexandros Karargyris, Tom J. Pollard, Alistair E. W. Johnson, Leo Anthony Celi |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/69292020cb6c4ee48dd996efb58bb8fb |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Automated abnormality classification of chest radiographs using deep convolutional neural networks
por: Yu-Xing Tang, et al.
Publicado: (2020) -
Smartphone-based symbol-digit modalities test reliably captures brain damage in multiple sclerosis
por: Linh Pham, et al.
Publicado: (2021) -
A lady with chest pain: Is there a clue in the chest radiograph?
por: Deepanjan Bhattacharya, et al.
Publicado: (2021) -
“Yes, but will it work for my patients?” Driving clinically relevant research with benchmark datasets
por: Trishan Panch, et al.
Publicado: (2020) -
Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19
por: Zaid Nabulsi, et al.
Publicado: (2021)