FGR-Net: interpretable fundus image gradeability classification based on deep reconstruction learning
The performance of diagnostic Computer-Aided Design (CAD) systems for retinal diseases depends on the quality of the retinal images being screened. Thus, many studies have been developed to evaluate and assess the quality of such retinal images. However, most of them did not investigate the relation...
Guardado en:
| Autores principales: | Khalid, Saif, Rashwan, Hatem A., Abdulwahab, Saddam, Abdel-Nasser, Mohamed, Quiroga, Facundo Manuel, Puig, Domenec |
|---|---|
| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/160108 |
| Aporte de: |
Ejemplares similares
-
Machine-learning-assisted insight into spin ice Dy2Ti2O7
por: Samarakoon, Anjana M., et al.
Publicado: (2020) -
Filtrando eventos de seguridad en forma conservativa mediante deep learning
por: Ferrado, Leandro, et al.
Publicado: (2016) -
The current role of machine learning and explainability in actuarial science
por: Lozano, Catalina, et al.
Publicado: (2021) -
Keyword Identification in Spanish Documents using Neural Networks
por: Aquino, Germán Osvaldo, et al.
Publicado: (2015) -
Bevacizumab intravítreo en el tratamiento de las oclusiones venosas de la retina
por: González-Castellanos, M. E., et al.
Publicado: (2017)