A deep data‐driven approach for enhanced segmentation of blood vessel for diabetic retinopathy
出版年份 2022 全文链接
标题
A deep data‐driven approach for enhanced segmentation of blood vessel for diabetic retinopathy
作者
关键词
-
出版物
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
Volume -, Issue -, Pages -
出版商
Wiley
发表日期
2022-03-16
DOI
10.1002/ima.22720
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Encoder Enhanced Atrous (EEA) Unet architecture for Retinal Blood vessel segmentation
- (2021) Sathananthavathi V. et al. Cognitive Systems Research
- Multi-path convolutional neural network in fundus segmentation of blood vessels
- (2020) Chun Tian et al. Biocybernetics and Biomedical Engineering
- Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network
- (2020) TV Roshini et al. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
- Domain-invariant interpretable fundus image quality assessment
- (2020) Yaxin Shen et al. MEDICAL IMAGE ANALYSIS
- Illumination normalized based technique for retinal blood vessel segmentation
- (2020) Sonali Dash et al. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
- Glaucoma assessment from color fundus images using convolutional neural network
- (2020) Poonguzhali Elangovan et al. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
- Lightweight image super-resolution with enhanced CNN
- (2020) Chunwei Tian et al. KNOWLEDGE-BASED SYSTEMS
- Stroke diagnosis from retinal fundus images using multi texture analysis
- (2019) R.S. Jeena et al. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
- Deep learning based enhanced tumor segmentation approach for MR brain images
- (2019) Mamta Mittal et al. APPLIED SOFT COMPUTING
- DUNet: A deformable network for retinal vessel segmentation
- (2019) Qiangguo Jin et al. KNOWLEDGE-BASED SYSTEMS
- CcNet: A cross-connected convolutional network for segmenting retinal vessels using multi-scale features
- (2019) Shouting Feng et al. NEUROCOMPUTING
- Parallel Architecture of Fully Convolved Neural Network for Retinal Vessel Segmentation
- (2019) Sathananthavathi .V et al. JOURNAL OF DIGITAL IMAGING
- Retinal Vessel Segmentation based on Fully Convolutional Neural Networks
- (2018) Américo Filipe Moreira Oliveira et al. EXPERT SYSTEMS WITH APPLICATIONS
- Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images
- (2018) Lei Zhou et al. IET Image Processing
- Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function
- (2018) Kai Hu et al. NEUROCOMPUTING
- Deep convolutional neural networks for diabetic retinopathy detection by image classification
- (2018) Shaohua Wan et al. COMPUTERS & ELECTRICAL ENGINEERING
- Segmenting Retinal Blood Vessels With _newline Deep Neural Networks
- (2016) Pawel Liskowski et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Hierarchical retinal blood vessel segmentation based on feature and ensemble learning
- (2015) Shuangling Wang et al. NEUROCOMPUTING
- Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification
- (2014) R.A. Welikala et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features
- (2014) Erkang Cheng et al. MACHINE VISION AND APPLICATIONS
- An effective retinal blood vessel segmentation method using multi-scale line detection
- (2012) Uyen T.V. Nguyen et al. PATTERN RECOGNITION
- Retinal Image Analysis Using Curvelet Transform and Multistructure Elements Morphology by Reconstruction
- (2010) M S Miri et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
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