Joint Optic Disc and Cup Segmentation Using Self-Supervised Multimodal Reconstruction Pre-Training
Use este enlace para citar
http://hdl.handle.net/2183/26438Colecciones
Metadatos
Mostrar el registro completo del ítemTítulo
Joint Optic Disc and Cup Segmentation Using Self-Supervised Multimodal Reconstruction Pre-TrainingFecha
2020-08-20Cita bibliográfica
Hervella, Á.S.; Ramos, L.; Rouco, J.; Novo, J.; Ortega, M. Joint Optic Disc and Cup Segmentation Using Self-Supervised Multimodal Reconstruction Pre-Training. Proceedings 2020, 54, 25. https://doi.org/10.3390/proceedings2020054025
Resumen
[Abstract]
The analysis of the optic disc and cup in retinal images is important for the early diagnosis of glaucoma. In order to improve the joint segmentation of these relevant retinal structures, we propose a novel approach applying the self-supervised multimodal reconstruction of retinal images as pre-training for deep neural networks. The proposed approach is evaluated on different public datasets. The obtained results indicate that the self-supervised multimodal reconstruction pre-training improves the performance of the segmentation. Thus, the proposed approach presents a great potential for also improving the interpretable diagnosis of glaucoma.
Palabras clave
Deep learning
Self-supervised learning
Segmentation
Eye fundus
Glaucoma
Self-supervised learning
Segmentation
Eye fundus
Glaucoma
Versión del editor
Derechos
Atribución 4.0 Internacional
ISSN
2504-3900