Diabetic Retinopathy Grading

16 papers with code • 1 benchmarks • 3 datasets

Grading the severity of diabetic retinopathy from (ophthalmic) fundus images

Most implemented papers

Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images

YijinHuang/Lesion-based-Contrastive-Learning 17 Jul 2021

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 27 Oct 2021

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.

OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing

retinal-research/redot 6 Feb 2023

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.

nnMobileNet: Rethinking CNN for Retinopathy Research

Retinal-Research/NNMOBILE-NET 2 Jun 2023

Over the past few decades, convolutional neural networks (CNNs) have been at the forefront of the detection and tracking of various retinal diseases (RD).

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.

Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images

agaldran/cost_sensitive_loss_classification 1 Oct 2020

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

lindehesse/insightr-net 31 Jul 2022

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 20 Oct 2022

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.