Diabetic Retinopathy Grading
19 papers with code • 1 benchmarks • 3 datasets
Grading the severity of diabetic retinopathy from (ophthalmic) fundus images
Benchmarks
These leaderboards are used to track progress in Diabetic Retinopathy Grading
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Kaggle EyePACS
DRTiD
Most implemented papers
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing
Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes.
Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images
YijinHuang/Lesion-based-Contrastive-Learning
•
•
Instead of taking entire images as the input in the common contrastive learning scheme, lesion patches are employed to encourage the feature extractor to learn representations that are highly discriminative for DR grading.
Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation
yijinhuang/pytorch-classification
•
•
To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components.
nnMobileNet: Rethinking CNN for Retinopathy Research
Retinal-Research/NNMOBILE-NET
•
•
Over the past few decades, convolutional neural networks (CNNs) have been at the forefront of the detection and tracking of various retinal diseases (RD).
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
ISS-Kerui/BIRA-NET-BILINEAR-ATTENTION-NET-FOR-DIABETIC-RETINOPATHY-GRADING
•
•
Diabetic retinopathy (DR) is a common retinal disease that leads to blindness.
Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
mikevoets/jama16-retina-replication
•
•
PLOS ONE 2019
We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets.
Robust Collaborative Learning of Patch-level and Image-level Annotations for Diabetic Retinopathy Grading from Fundus Image
As a result, it exploits more discriminative features for DR grading.
Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images
agaldran/cost_sensitive_loss_classification
•
•
Assessing the degree of disease severity in biomedical images is a task similar to standard classification but constrained by an underlying structure in the label space.
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical Examples
Convolutional neural networks (CNNs) have shown exceptional performance for a range of medical imaging tasks.
SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy Grading
yijinhuang/ssit
•
•
Specifically, two saliency-guided learning tasks are employed in SSiT: (1) Saliency-guided contrastive learning is conducted based on the momentum contrast, wherein fundus images' saliency maps are utilized to remove trivial patches from the input sequences of the momentum-updated key encoder.
