AIMC Topic: Fundus Oculi
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Atrous residual convolutional neural network based on U-Net for retinal vessel segmentation.
PloS one
Aug 22, 2022
Extracting features of retinal vessels from fundus images plays an essential role in computer-aided diagnosis of diseases, such as diabetes, hypertension, and cerebrovascular diseases. Although a number of deep learning-based methods have been used i...
Multi-label classification of fundus images with graph convolutional network and LightGBM.
Computers in biology and medicine
Aug 11, 2022
Early detection and treatment of retinal disorders are critical for avoiding irreversible visual impairment. Given that patients in the clinical setting may have various types of retinal illness, the development of multi-label fundus disease detectio...
Semi-supervised classification of fundus images combined with CNN and GCN.
Journal of applied clinical medical physics
Aug 10, 2022
PURPOSE: Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and diffe...
Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives.
Progress in retinal and eye research
Aug 4, 2022
When it first appeared, multimodal fundus imaging revolutionized the diagnostic workup and provided extremely useful new insights into the pathogenesis of fundus diseases. The recent addition of quantitative approaches has further expanded the amount...
Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation.
IEEE transactions on medical imaging
Aug 1, 2022
Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably lose the edge information, which contains spatial features of vessels...
Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images.
The British journal of ophthalmology
Jul 27, 2022
BACKGROUND: The efficiency of clinical trials for retinitis pigmentosa (RP) treatment is limited by the screening burden and lack of reliable surrogate markers for functional end points. Automated methods to determine visual acuity (VA) may help addr...
Multi-task deep learning for glaucoma detection from color fundus images.
Scientific reports
Jul 20, 2022
Glaucoma is an eye condition that leads to loss of vision and blindness if not diagnosed in time. Diagnosis requires human experts to estimate in a limited time subtle changes in the shape of the optic disc from retinal fundus images. Deep learning m...
Single-shot retinal image enhancement using untrained and pretrained neural networks priors integrated with analytical image priors.
Computers in biology and medicine
Jul 14, 2022
Retinal images acquired using fundus cameras are often visually blurred due to imperfect imaging conditions, refractive medium turbidity, and motion blur. In addition, ocular diseases such as the presence of cataracts also result in blurred retinal i...
MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN.
Sensors (Basel, Switzerland)
Jun 17, 2022
Retinal vessel segmentation is extremely important for risk prediction and treatment of many major diseases. Therefore, accurate segmentation of blood vessel features from retinal images can help assist physicians in diagnosis and treatment. Convolut...
A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs.
BioMed research international
Jun 7, 2022
Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings publi...
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