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Found 7 papers, 0 papers with code
ConKeD++ -- Improving descriptor learning for retinal image registration: A comprehensive study of contrastive losses
In this work, we propose to test and extend and improve a state-of-the-art framework for color fundus image registration, ConKeD.
ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration
In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration.
Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning
This is possible thanks to the use of a CNN with a custom setting that links the lesions and the diagnosis.
Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images
The proposed multi-segmentation method allows to detect more vessels and better segment the different structures, while achieving a competitive classification performance.
Improving AMD diagnosis by the simultaneous identification of associated retinal lesions
To overcome these issues, several works have proposed automatic methods for the detection of AMD in retinography images, the most widely used modality for the screening of the disease.
Multimodal Transfer Learning-based Approaches for Retinal Vascular Segmentation
These two issues become specially relevant when applying FCNs to medical image segmentation as, first, the existent models are usually adjusted from broad domain applications over photographic images, and second, the amount of annotated data is usually scarcer.
Multimodal Registration of Retinal Images Using Domain-Specific Landmarks and Vessel Enhancement
The analysis of different image modalities is frequently performed in ophthalmology as it provides complementary information for the diagnosis and follow-up of relevant diseases, like hypertension or diabetes.
