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Recent trends and advances in fundus image analysis: A review
Automated retinal image analysis holds prime significance in the accurate diagnosis of
various critical eye diseases that include diabetic retinopathy (DR), age-related macular …
various critical eye diseases that include diabetic retinopathy (DR), age-related macular …
[HTML][HTML] Machine learning empowering personalized medicine: A comprehensive review of medical image analysis methods
Artificial intelligence (AI) advancements, especially deep learning, have significantly
improved medical image processing and analysis in various tasks such as disease …
improved medical image processing and analysis in various tasks such as disease …
Segment anything model for medical images?
Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …
segmentation. It has achieved impressive results on various natural image segmentation …
Scs-net: A scale and context sensitive network for retinal vessel segmentation
Accurately segmenting retinal vessel from retinal images is essential for the detection and
diagnosis of many eye diseases. However, it remains a challenging task due to (1) the large …
diagnosis of many eye diseases. However, it remains a challenging task due to (1) the large …
CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging
Automated detection of curvilinear structures, eg, blood vessels or nerve fibres, from medical
and biomedical images is a crucial early step in automatic image interpretation associated to …
and biomedical images is a crucial early step in automatic image interpretation associated to …
DUNet: A deformable network for retinal vessel segmentation
Automatic segmentation of retinal vessels in fundus images plays an important role in the
diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose …
diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose …
ROSE: a retinal OCT-angiography vessel segmentation dataset and new model
Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique
that has been increasingly used to image the retinal vasculature at capillary level resolution …
that has been increasingly used to image the retinal vasculature at capillary level resolution …
Dual encoder-based dynamic-channel graph convolutional network with edge enhancement for retinal vessel segmentation
Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for
clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably …
clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably …
Lightweight attention convolutional neural network for retinal vessel image segmentation
Retinal vessel image is an important biological information that can be used for personal
identification in the social security domain, and for disease diagnosis in the medical domain …
identification in the social security domain, and for disease diagnosis in the medical domain …
Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation
Objective: Deep learning based methods for retinal vessel segmentation are usually trained
based on pixel-wise losses, which treat all vessel pixels with equal importance in pixel-to …
based on pixel-wise losses, which treat all vessel pixels with equal importance in pixel-to …