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Found 7 papers, 7 papers with code
SwinVFTR: A Novel Volumetric Feature-learning Transformer for 3D OCT Fluid Segmentation
To address these issues, we propose SwinVFTR, a new transformer-based architecture designed for precise fluid segmentation in 3D volumetric OCT images.
Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images
Ophthalmic images may contain identical-looking pathologies that can cause failure in automated techniques to distinguish different retinal degenerative diseases.
VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers
The only non-invasive method for capturing retinal vasculature is optical coherence tomography-angiography (OCTA).
RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network
In order to avoid the loss of fidelity suffered by traditional GAN-based segmentation systems, we introduce a novel weighted feature matching loss.
Ranked #2 on
Retinal Vessel Segmentation
on STARE
Generative Adversarial Network
Retinal Vessel Segmentation
+2
Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images using Generative Adversarial Networks
Fluorescein Angiography (FA) is a technique that employs the designated camera for Fundus photography incorporating excitation and barrier filters.
Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
Noisy data and the similarity in the ocular appearances caused by different ophthalmic pathologies pose significant challenges for an automated expert system to accurately detect retinal diseases.
Ranked #1 on
Retinal OCT Disease Classification
on Srinivasan2014
Fundus2Angio: A Conditional GAN Architecture for Generating Fluorescein Angiography Images from Retinal Fundus Photography
Angiography requires insertion of a dye that may cause severe adverse effects and can even be fatal.
Fundus to Angiography Generation
Generative Adversarial Network
