Diabetic Retinopathy Grading
9 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 |
|---|---|---|---|---|---|
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Kaggle EyePACS
DRTiD
Most implemented papers
Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation
yijinhuang/pytorch-classification
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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.
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
ISS-Kerui/BIRA-NET-BILINEAR-ATTENTION-NET-FOR-DIABETIC-RETINOPATHY-GRADING
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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
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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
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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.
Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images
YijinHuang/Lesion-based-Contrastive-Learning
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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.
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
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Specifically, two saliency-guided learning tasks are employed in SSiT: (1) We conduct saliency-guided contrastive learning based on the momentum contrast, wherein we utilize fundus images' saliency maps to remove trivial patches from the input sequences of the momentum-updated key encoder.
Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images
fdu-vts/drtid
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However, automatic DR grading based on two-field fundus photography remains a challenging task due to the lack of publicly available datasets and effective fusion strategies.
