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Paired and Unpaired Deep Generative Models on Multimodal Retinal Image Reconstruction
(M D P I AG, 2019-08-07)
[Abstract] This work explores the use of paired and unpaired data for training deep neural networks in the multimodal reconstruction of retinal images. Particularly, we focus on the reconstruction of fluorescein angiography ...
Fully Automatic Retinal Vascular Tortuosity Assessment Integrating Domain-Related Information
(MDPI AG, 2020-08-21)
[Abstract]
The fundus of the eye is the only part of the human body that allows a direct non-invasive observation of the circulatory system. Retinal vascular tortuosity presents a valuable potential for diagnostic and ...
Joint Optic Disc and Cup Segmentation Using Self-Supervised Multimodal Reconstruction Pre-Training
(MDPI AG, 2020-08-20)
[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 ...
Context encoder self-supervised approaches for eye fundus analysis
(Institute of Electrical and Electronics Engineers Inc., 2021)
[Abstract]: The broad availability of medical images in current clinical practice provides a source of large image datasets. In order to use these datasets for training deep neural networks in detection and segmentation ...