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Found 8 papers, 3 papers with code
RRWNet: Recursive Refinement Network for Effective Retinal Artery/Vein Segmentation and Classification
The framework consists of a fully convolutional neural network that recursively refines semantic segmentation maps, correcting manifest classification errors and thus improving topological consistency.
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Deep Multimodal Fusion of Data with Heterogeneous Dimensionality via Projective Networks
The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases.
SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT
The Segment Anything Model (SAM) has gained significant attention in the field of image segmentation due to its impressive capabilities and prompt-based interface.
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation
Moreover, the proposed SSL method allows further improvement of this performance by up to 23%, and we show that the SSL is beneficial regardless of the network architecture.
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.
