Titlebook: Ophthalmic Medical Image Analysis; 6th International Wo Huazhu Fu,Mona K. Garvin,Yalin Zheng Conference proceedings 2019 Springer Nature Sw

撒謊

書目名稱Ophthalmic Medical Image Analysis影響因子(影響力)




書目名稱Ophthalmic Medical Image Analysis影響因子(影響力)學(xué)科排名




書目名稱Ophthalmic Medical Image Analysis網(wǎng)絡(luò)公開度




書目名稱Ophthalmic Medical Image Analysis網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Ophthalmic Medical Image Analysis被引頻次




書目名稱Ophthalmic Medical Image Analysis被引頻次學(xué)科排名




書目名稱Ophthalmic Medical Image Analysis年度引用




書目名稱Ophthalmic Medical Image Analysis年度引用學(xué)科排名




書目名稱Ophthalmic Medical Image Analysis讀者反饋




書目名稱Ophthalmic Medical Image Analysis讀者反饋學(xué)科排名

吸引力

Structure-Aware Noise Reduction Generative Adversarial Network for Optical Coherence Tomography Imasis. However, image quality still suffers from speckle noise and other motion artifacts. An effective OCT denoising method is needed to ensure the image is interpreted correctly. However, lack of paired clean image restricts its development. Here, we propose an end-to-end structure-aware noise reduc

Ligneous

Region-Based Segmentation of Capillary Density in Optical Coherence Tomography Angiography, allows visualization and analysis of the retinal microvascular network in a non-invasive way. However, automated analysis of microvascular changes in OCTA is not a trivial task. Current approaches often attempt to directly segment the microvasculature. These approaches generally have problems in ca

Monotonous

CRUMB

分期付款

金絲雀

3D-CNN for Glaucoma Detection Using Optical Coherence Tomography,e GPU in its original resolution. The direct analysis of these volumes however, provides advantages such as circumventing the need for the segmentation of retinal structures. Previously, a deep learning (DL) approach was proposed for the detection of glaucoma directly from 3D OCT volumes, where the

overbearing

MORT

Shape Decomposition of Foveal Pit Morphology Using Scan Geometry Corrected OCT,e of the foveal pit in the human retina is still largely unknown. In this study we analyze the shape morphology of the foveal pit using a statistical shape model to find the principal shape variations in a cohort of 50 healthy subjects. Our analysis includes the use of scan geometry correction to re

Asperity