Diabetic Retinopathy Grading

9 papers with code • 1 benchmarks • 3 datasets

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

Most implemented papers

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.

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.

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.

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) 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 26 Nov 2022

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.