Automated Identification of Diabetic Retinopathy Using Deep Learning.

By Rishab Gargeya, Theodore Leng. Ophthalmology (Rochester, Minn.), 2017

1,233 citations recorded by the source corpus.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5cdfbb95a0ff42171b03493f5c59772b47ef3385

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ophthalmology

1. Daniel Shu Wei Ting
2. Huan Fu
3. Kai Wang
4. Tien Yin Wong
5. Tao Li

In Radiology, Nuclear Medicine and Imaging

1. P. Heng
2. Lequan Yu
3. Daniel Shu Wei Ting
4. Chi-Wing Fu
5. Huan Fu
6. Kai Wang
7. Tien Yin Wong
8. Dale R. Webster

Citations in the corpus

• CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading (2020, background): “Deep learning, especially with CNNs, provides powerful support for DR grading [15-17].”
• A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection (2019, methodology): “The computer vision-based methods are divided into hand-on engineering [4]–[7] and end-to-end learning [8]–[10].”
• Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes (2017, methodology): “A few DLSs have recently shown high sensitivity and specificity (>90%) in detecting referable diabetic retinopathy from retinal photographs, primarily using high-quality images from publicly available databases from homogenous populations of white individuals.(10-12) The performance of a DLS in scre”
• Artificial intelligence in retina. (2018, methodology): “13) (Gargeya and Leng, 2017).”
• Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. (2018, background): “Recent evidence suggests that deep learning algorithms can grade images with excellent diagnostic accuracy in identifying conditions such as diabetic retinopathy.(21,22) If this technology could be adopted to provide accurate assessment of glaucoma, there are significant potential benefits including”
• Applications of Deep Learning in Fundus Images: A Review (2021, methodology): “Gargeya and Leng (2017) also used a CNN to perform DR binary classification. They used a CNN containing five residual blocks to extract image features. The features extracted by the deep CNN and metadata information were fed into a decision tree model for binary classification. Li et al. (2018b) use”
• Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective (2020, background): “Since 2016, many groups have published on the application of DL for DR screening (Table 2) (Gulshan et al., 2016; Ting et al., 2017; Gargeya and Leng, 2017).”
• CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading (2019, methodology): “Gargeya and Leng [34] designed a CNN-based model for DR severity measurements.”