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CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading

https://doi.org/10.1109/tmi.2019.2951844

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

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Observations and Lessons Learned From the Artificial Intelligence Studies for Diabetic Retinopathy Screening

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MTMR-Net: Multi-task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis

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A Framework for Identifying Diabetic Retinopathy Based on Anti-noise Detection and Attention-Based Fusion

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CBAM: Convolutional Block Attention Module

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Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities

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Kernel Generalized-Gaussian Mixture Model for Robust Abnormality Detection

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Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection

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Retinal Microaneurysm Detection Using Clinical Report Guided Multi-sieving CNN

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Lesion Detection and Grading of Diabetic Retinopathy via Two-Stages Deep Convolutional Neural Networks

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Computational Methods for Exudates Detection and Macular Edema Estimation in Retinal Images: A Survey

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Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index

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Full left ventricle quantification via deep multitask relationships learning

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Diabetic Macular Edema: Pathophysiology and Novel Therapeutic Targets

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Automated Identification of Diabetic Retinopathy Using Deep Learning

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Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy

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Convolutional Neural Networks for Diabetic Retinopathy

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Diabetic Macular Edema

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Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading

inferredcites →https://doi.org/10.1109/access.2018.2873415openalex-snapshot

Fundus Images-Based Detection and Grading of Macular Edema Using Robust Macula Localization

inferredcites →https://doi.org/10.1109/cisp-bmei.2017.8301998openalex-snapshot

Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification

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Deep Residual Learning for Image Recognition

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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

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Densely Connected Convolutional Networks

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Squeeze-and-Excitation Networks

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Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images

inferredcites →https://doi.org/10.1109/embc.2018.8512828openalex-snapshot

Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading

inferredcites →https://doi.org/10.1109/icassp.2018.8461427openalex-snapshot

Diabetic Retinopathy Detection Based on Deep Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/isbi.2018.8363791openalex-snapshot

Deep multi-task and task-specific feature learning network for robust shape preserved organ segmentation

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New Deep Neural Nets for Fine-Grained Diabetic Retinopathy Recognition on Hybrid Color Space

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DREAM: Diabetic Retinopathy Analysis Using Machine Learning

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Beyond Lesion-Based Diabetic Retinopathy: A Direct Approach for Referral

inferredcites →https://doi.org/10.1109/tmi.2015.2509785openalex-snapshot

Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening

inferredcites →https://doi.org/10.1109/tmi.2016.2526689openalex-snapshot

Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images

inferredcites →https://doi.org/10.1109/tpami.2019.2913372openalex-snapshot

Squeeze-and-Excitation Networks

inferredcites →https://doi.org/10.1111/ceo.13056openalex-snapshot

Diabetic retinopathy screening using deep neural network

inferredcites →https://doi.org/10.1167/iovs.10-6633openalex-snapshot

Evaluation of a Computer-Aided Diagnosis System for Diabetic Retinopathy Screening on Public Data

inferredcites →https://doi.org/10.1201/9781003031352-6openalex-snapshot

Deep CNN-based early detection and grading of diabetic retinopathy using retinal fundus images

inferredcites →https://doi.org/10.1243/09544119jeim486openalex-snapshot

Computer-based detection of diabetes retinopathy stages using digital fundus images

inferredcites →https://doi.org/10.17077/omia.1055openalex-snapshot

Diabetic Macular Edema Grading Based on Deep Neural Networks

inferredcites →https://doi.org/10.3233/thc-174704openalex-snapshot

Diabetic macular edema grading in retinal images using vector quantization and semi-supervised learning

inferredcites →https://doi.org/10.3390/data3030025openalex-snapshot

Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

inferredcites →https://doi.org/10.3390/s90302148openalex-snapshot

Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering

inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot

AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.48550/arxiv.1609.03126openalex-snapshot

Energy-based Generative Adversarial Network

inferredcites →https://doi.org/10.48550/arxiv.1809.04228openalex-snapshot

Ensemble of Convolutional Neural Networks for Automatic Grading of Diabetic Retinopathy and Macular Edema

inferredcites →https://doi.org/10.48550/arxiv.1812.10595openalex-snapshot

Deep Learning based Early Detection and Grading of Diabetic Retinopathy Using Retinal Fundus Images

inferredcites →https://doi.org/10.48550/arxiv.1905.07203openalex-snapshot

Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset

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FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Multi-Spectrum Information Bottleneck Network (MIBN) for Classification of Diabetic Retinopathy on the Basis of Multicolor Image

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OcuViT: Automated Detection of Diabetic Retinopathy and AMD Using a Hybrid Vision Transformer Approach

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Deep Learning‐Based Image Segmentation for Early Detection of Diabetic Retinopathy and Other Retinal Disorders

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Prevention of Blindness for Diabetic Patients Using Deep Learning Techniques

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Diabetic retinopathy severity grading employing quadrant‐based Inception‐V3 convolution neural network architecture

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Aggregated residual transformation network for multistage classification in diabetic retinopathy

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A comprehensive review on automated systems for severity grading of diabetic retinopathy and macular edema

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Multi‐Deep Learning Approach With Transfer Learning for 7‐Stages Diabetic Retinopathy Classification

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Problem‐Oriented Strategy for Diabetic Retinopathy Identification

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Adaptive Evidential Fusion of Light–Dark Features With Multi‐Scan Mamba for Automated Macular Edema Diagnosis

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A new retinal OCT‐angiography diabetic retinopathy dataset for segmentation and DR grading

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Coinciding Diabetic Retinopathy and Diabetic Macular Edema Grading With Rat Swarm Optimization Algorithm for Enhanced Capsule Generation Adversarial Network

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Deep learning technology in vascular image segmentation and disease diagnosis

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Self-Attention Mechanism for Diabetic Retinopathy Detection

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Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications

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GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy

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DR Detection Using Optical Coherence Tomography Angiography (OCTA): A Transfer Learning Approach with Robustness Analysis

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MBNet: A Multi-task Deep Neural Network for Semantic Segmentation and Lumbar Vertebra Inspection on X-Ray Images

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Triplet-Branch Network with Prior-Knowledge Embedding for Fatigue Fracture Grading

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Dformer: Dual-Path Transformers for Geometric and Appearance Features Reasoning in Diabetic Retinopathy Grading

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Deep Learning Approach to Identify Diabetic Retinopathy Severity and Progression Using Ultra-Wide Field Retinal Images

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Analyzing Adjacent B-Scans to Localize Sickle Cell Retinopathy In OCTs

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EfficientNet-B1 Based Diabetic Retinopathy Detection from Ultra-widefield Fundus Images

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Many-MobileNet: Multi-model Augmentation for Robust Retinal Disease Classification

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DME-MobileNet: Fine-Tuning nnMobileNet for Diabetic Macular Edema Classification

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Deep Learning-Based Analysis of Ocular Fundus Images for Diabetic Retinopathy Detection

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Automated Diabetic Retinopathy Screening Using Deep Learning and Transfer Learning Approaches

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Joint grading of diabetic retinopathy and diabetic macular edema using an adaptive attention block and semisupervised learning

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Automated seven-stage diabetic retinopathy grading using optimized deep networks through systematic hyperparameter tuning

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Deep learning for diabetic retinopathy assessments: a literature review

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Eye diseases detection using deep learning with BAM attention module

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Contrastive self-supervised learning for diabetic retinopathy early detection

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Effective methods of diabetic retinopathy detection based on deep convolutional neural networks

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GCN-based MIL: multi-instance learning utilizing structural relationships among instances

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NAMN: Normalization-based attention multimodel network for diabetic macular edema grading from multicolor image

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A Systematic Review on Diabetic Retinopathy Detection Using Deep Learning Techniques

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inferred← citeshttps://doi.org/10.1007/s11831-025-10376-8openalex-snapshot

Advances in Deep Neural, Transformer Learning, and Kernel-Based Methods for Diabetic Retinopathy Detection: A Comprehensive Review

inferred← citeshttps://doi.org/10.1007/s11831-026-10502-0openalex-snapshot

Revolutionizing Wheat Plant Disease Detection: A Review of Imaging, AI, and Innovations

inferred← citeshttps://doi.org/10.1007/s11892-022-01467-yopenalex-snapshot

Artificial Intelligence Algorithms in Diabetic Retinopathy Screening

inferred← citeshttps://doi.org/10.1007/s12652-023-04648-zopenalex-snapshot

Ensemble classification based optimized transfer learning feature method for early stage diagnosis of diabetic retinopathy

inferred← citeshttps://doi.org/10.1007/s13410-024-01437-yopenalex-snapshot

A hybrid model for diabetic retinopathy and diabetic macular edema severity grade classification

inferred← citeshttps://doi.org/10.1007/s13748-026-00452-wopenalex-snapshot

Modified MAF-U-Net: joint segmentation of optic disc and cup from fundus images

inferred← citeshttps://doi.org/10.1007/s13755-022-00181-zopenalex-snapshot

Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy

inferred← citeshttps://doi.org/10.1007/s13755-022-00209-4openalex-snapshot

MCA-UNet: multi-scale cross co-attentional U-Net for automatic medical image segmentation

inferred← citeshttps://doi.org/10.1007/s13755-026-00457-8openalex-snapshot

An explainable hybrid deep-learning and machine learning framework for automatic coeliac disease detection from duodenal endoscopy images

inferred← citeshttps://doi.org/10.1007/s42835-024-01798-8openalex-snapshot

Performance Analysis of Diabetic Retinopathy and Hemorrhages Segmentation System Using Enhanced Visual Geometry Group (EVGG) Architecture

inferred← citeshttps://doi.org/10.1007/s42979-021-00833-zopenalex-snapshot

A Survey on Automatic Diabetic Retinopathy Screening

inferred← citeshttps://doi.org/10.1007/s42979-025-04266-wopenalex-snapshot

An Attention Driven Retinal Classification Model for the Joint Grading of Diabetic Retinopathy and Macular Edema

inferred← citeshttps://doi.org/10.1007/s42979-026-05001-9openalex-snapshot

Quad Branch Fusion (QBFAM) and Grouped Convolutional-Spatial (GCSAM) Deep Learning Attention Modules for Retinal Image’s Segmentation

inferred← citeshttps://doi.org/10.1007/s44196-025-01021-0openalex-snapshot

Deep Learning-Based Interpretable Framework for Simultaneous Segmentation of Retinal Multi-lesions and Multi-categorical Disease Classification

inferred← citeshttps://doi.org/10.1016/j.aej.2023.10.040openalex-snapshot

Segmentation of diabetic retinopathy images using deep feature fused residual with U-Net

inferred← citeshttps://doi.org/10.1016/j.ailsci.2022.100043openalex-snapshot

HematoNet: Expert level classification of bone marrow cytology morphology in hematological malignancy with deep learning

inferred← citeshttps://doi.org/10.1016/j.artmed.2024.102782openalex-snapshot

An interpretable dual attention network for diabetic retinopathy grading: IDANet

inferred← citeshttps://doi.org/10.1016/j.artmed.2024.102801openalex-snapshot

Triplet-branch network with contrastive prior-knowledge embedding for disease grading

inferred← citeshttps://doi.org/10.1016/j.asoc.2020.106328openalex-snapshot

Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models

inferred← citeshttps://doi.org/10.1016/j.asoc.2022.109462openalex-snapshot

Classification of diabetic retinopathy with feature selection over deep features using nature-inspired wrapper methods

inferred← citeshttps://doi.org/10.1016/j.bbe.2022.12.005openalex-snapshot

Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review

inferred← citeshttps://doi.org/10.1016/j.bspc.2021.103423openalex-snapshot

Diabetic Retinopathy Grading by a Source-Free Transfer Learning Approach

inferred← citeshttps://doi.org/10.1016/j.bspc.2021.103439openalex-snapshot

Joint DR-DME classification using deep learning-CNN based modified grey-wolf optimizer with variable weights

inferred← citeshttps://doi.org/10.1016/j.bspc.2021.103471openalex-snapshot

GLCM and statistical features extraction technique with Extra-Tree Classifier in Macular Oedema risk diagnosis

inferred← citeshttps://doi.org/10.1016/j.bspc.2022.103874openalex-snapshot

Siamese network based fine grained classification for Diabetic Retinopathy grading

inferred← citeshttps://doi.org/10.1016/j.bspc.2022.103952openalex-snapshot

OHGCNet: Optimal feature selection-based hybrid graph convolutional network model for joint DR-DME classification

inferred← citeshttps://doi.org/10.1016/j.bspc.2022.104277openalex-snapshot

Deep metric learning with mirror attention and fine triplet loss for fundus image retrieval in ophthalmology

inferred← citeshttps://doi.org/10.1016/j.bspc.2022.104365openalex-snapshot

Automatic Classification of Retinal Diseases with Transfer Learning-Based Lightweight Convolutional Neural Network

inferred← citeshttps://doi.org/10.1016/j.bspc.2022.104386openalex-snapshot

Auto-Metric Graph Neural Network optimized with Capuchin search optimization algorithm for coinciding diabetic retinopathy and diabetic Macular edema grading

inferred← citeshttps://doi.org/10.1016/j.bspc.2023.104830openalex-snapshot

Eye tracking based deep learning analysis for the early detection of diabetic retinopathy: A pilot study

inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105607openalex-snapshot

Lesion identification in fundus images via convolutional neural network-vision transformer

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106045openalex-snapshot

Cross-Image Siamese Graph Convolutional Network For Fine-Grained Image Retrieval in Diabetic Retinopathy

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106252openalex-snapshot

Classification of diabetic retinopathy based on Functional Linked Neural network utilizing segmented fundus image features

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106714openalex-snapshot

MSEF-Net: A multi-scale EfficientNet Fusion for Diabetic Retinopathy grading

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106777openalex-snapshot

Discriminative analysis of diabetic retinopathy using cascaded network with Atrous convolution and fundus biomarkers

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106894openalex-snapshot

Honey Badger Aquila optimization-based deep learning with multi-kernel shape index histograms for diabetic macular edema classification

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.107352openalex-snapshot

STMF-DRNet: A multi-branch fine-grained classification model for diabetic retinopathy using Swin-TransformerV2

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.107804openalex-snapshot

Advancing diabetic retinopathy classification using ensemble deep learning approaches

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.109167openalex-snapshot

Semi-supervised multi-task learning for interpretable quality assessment of fundus images

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.110133openalex-snapshot

Dynamic domain-adaptive collaborative learning for diabetic retinopathy grading

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.110522openalex-snapshot

Ordinal-aware logit adjustment and balanced representation learning for diabetic retinopathy grading

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.110603openalex-snapshot

A multimodal fusion framework for diabetic retinopathy grading integrating fundus images and structured clinical data

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.111484openalex-snapshot

CDNet: A collaboration and disentanglement network for cross-domain joint grading of Diabetic Retinopathy and Diabetic Macular Edema

inferred← citeshttps://doi.org/10.1016/j.cmpb.2023.107747openalex-snapshot

Deep multi-task learning for nephropathy diagnosis on immunofluorescence images

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2021.104599openalex-snapshot

Deep learning for diabetic retinopathy detection and classification based on fundus images: A review

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.105302openalex-snapshot

Multimodal image encoding pre-training for diabetic retinopathy grading

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.105341openalex-snapshot

Collaborative learning of weakly-supervised domain adaptation for diabetic retinopathy grading on retinal images

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.105602openalex-snapshot

Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.105970openalex-snapshot

Resampling-based cost loss attention network for explainable imbalanced diabetic retinopathy grading

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.106496openalex-snapshot

Multi-task deep learning for medical image computing and analysis: A review

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.107993openalex-snapshot

CRA-Net: Transformer guided category-relation attention network for diabetic retinopathy grading

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108002openalex-snapshot

TBACkp: HER2 expression status classification network focusing on intrinsic subenvironmental characteristics of breast cancer liver metastases

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108523openalex-snapshot

Diabetic retinopathy prediction based on vision transformer and modified capsule network

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.109179openalex-snapshot

A multi-task learning model for clinically interpretable sesamoiditis grading

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.109352openalex-snapshot

Multi-lesion segmentation guided deep attention network for automated detection of diabetic retinopathy

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.109656openalex-snapshot

ResViT FusionNet Model: An explainable AI-driven approach for automated grading of diabetic retinopathy in retinal images

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2024.109719openalex-snapshot

Detection of artificial spots in fundus images using modified U-Net based semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2024.109746openalex-snapshot

MPLNet: Multi-task supervised progressive learning network for diabetic retinopathy grading

inferred← citeshttps://doi.org/10.1016/j.compmedimag.2024.102370openalex-snapshot

MEF-UNet: An end-to-end ultrasound image segmentation algorithm based on multi-scale feature extraction and fusion

inferred← citeshttps://doi.org/10.1016/j.engappai.2024.107994openalex-snapshot

GNN-fused CapsNet with multi-head prediction for diabetic retinopathy grading

inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111795openalex-snapshot

Enhancing fundus image diabetic retinopathy classification through modified conformer with sparse attention

inferred← citeshttps://doi.org/10.1016/j.engappai.2026.116049openalex-snapshot

A Stacked Ensemble Framework with Global Context Vision Transformer Feature Integration for Accurate Diabetic Retinopathy Detection

inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115068openalex-snapshot

Artificial intelligence in ophthalmopathy and ultra-wide field image: A survey

inferred← citeshttps://doi.org/10.1016/j.eswa.2022.118835openalex-snapshot

Automatic grading of Diabetic macular edema based on end-to-end network

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.120615openalex-snapshot

Hyperspectral pathology image classification using dimension-driven multi-path attention residual network

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.122889openalex-snapshot

DCAMIL: Eye-tracking guided dual-cross-attention multi-instance learning for refining fundus disease detection

inferred← citeshttps://doi.org/10.1016/j.heliyon.2023.e17217openalex-snapshot

Fine-grained attention & knowledge-based collaborative network for diabetic retinopathy grading

inferred← citeshttps://doi.org/10.1016/j.icte.2025.08.001openalex-snapshot

Deep learning-based diabetic retinopathy recognition and grading: Challenges, gaps, and an improved approach — A survey

inferred← citeshttps://doi.org/10.1016/j.ijcce.2023.02.002openalex-snapshot

Diabetic retinopathy detection using principal component analysis multi-label feature extraction and classification

inferred← citeshttps://doi.org/10.1016/j.imu.2020.100377openalex-snapshot

Diabetic retinopathy detection through deep learning techniques: A review

inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103476openalex-snapshot

Graph mapping Mamba network for automated macular edema diagnosis from fundus images

inferred← citeshttps://doi.org/10.1016/j.jksuci.2023.101719openalex-snapshot

A comprehensive computer-aided system for an early-stage diagnosis and classification of diabetic macular edema

inferred← citeshttps://doi.org/10.1016/j.jot.2022.07.004openalex-snapshot

A novel image-based machine learning model with superior accuracy and predictability for knee arthroplasty loosening detection and clinical decision making

inferred← citeshttps://doi.org/10.1016/j.jvcir.2023.103956openalex-snapshot

MIL-ViT: A multiple instance vision transformer for fundus image classification

inferred← citeshttps://doi.org/10.1016/j.knosys.2022.108877openalex-snapshot

Cross-granularity multi-task network for ischemia diagnosis and defect detection in the myocardial perfusion imaging

inferred← citeshttps://doi.org/10.1016/j.knosys.2024.111764openalex-snapshot

Adaptive feature selection for active trachoma image classification

inferred← citeshttps://doi.org/10.1016/j.matpr.2022.01.466openalex-snapshot

Binary Classification of DR-Diabetic Retinopathy using CNN with Fundus Colour Images

inferred← citeshttps://doi.org/10.1016/j.matpr.2022.02.162openalex-snapshot

Computer aided diagnosis systems using deep learning for retinal diseases: A survey

inferred← citeshttps://doi.org/10.1016/j.measurement.2022.111485openalex-snapshot

A novel few-shot classification framework for diabetic retinopathy detection and grading

inferred← citeshttps://doi.org/10.1016/j.media.2021.101971openalex-snapshot

Applications of deep learning in fundus images: A review

inferred← citeshttps://doi.org/10.1016/j.media.2021.101985openalex-snapshot

A survey on incorporating domain knowledge into deep learning for medical image analysis

inferred← citeshttps://doi.org/10.1016/j.media.2021.102031openalex-snapshot

Cross-attention multi-branch network for fundus diseases classification using SLO images

inferred← citeshttps://doi.org/10.1016/j.media.2024.103214openalex-snapshot

Confidence-aware multi-modality learning for eye disease screening

inferred← citeshttps://doi.org/10.1016/j.media.2024.103357openalex-snapshot

A Foundation Language-Image Model of the Retina (FLAIR): encoding expert knowledge in text supervision

inferred← citeshttps://doi.org/10.1016/j.media.2025.103511openalex-snapshot

MSTNet: Multi-scale spatial-aware transformer with multi-instance learning for diabetic retinopathy classification

inferred← citeshttps://doi.org/10.1016/j.neucom.2023.126245openalex-snapshot

Disease-grading networks with ordinal regularization for medical imaging

inferred← citeshttps://doi.org/10.1016/j.neunet.2020.09.005openalex-snapshot

AMD-GAN: Attention encoder and multi-branch structure based generative adversarial networks for fundus disease detection from scanning laser ophthalmoscopy images

inferred← citeshttps://doi.org/10.1016/j.neunet.2025.107168openalex-snapshot

DVPT: Dynamic Visual Prompt Tuning of large pre-trained models for medical image analysis

inferred← citeshttps://doi.org/10.1016/j.neunet.2025.108011openalex-snapshot

Multimodal self-supervised retinal vessel segmentation

inferred← citeshttps://doi.org/10.1016/j.patcog.2021.108104openalex-snapshot

MVDRNet: Multi-view diabetic retinopathy detection by combining DCNNs and attention mechanisms

inferred← citeshttps://doi.org/10.1016/j.pdpdt.2026.105600openalex-snapshot

Joint multi-task deep learning with cross-task attention for simultaneous lesion segmentation, detection, and severity grading of diabetic retinopathy on fundus images: An external validation study

inferred← citeshttps://doi.org/10.1016/j.survophthal.2022.08.004openalex-snapshot

Artificial intelligence in screening, diagnosis, and classification of diabetic macular edema: A systematic review

inferred← citeshttps://doi.org/10.1038/s41433-021-01552-8openalex-snapshot

Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs

inferred← citeshttps://doi.org/10.1038/s41598-023-28347-zopenalex-snapshot

Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings

inferred← citeshttps://doi.org/10.1038/s41598-024-56389-4openalex-snapshot

A graph convolutional network with dynamic weight fusion of multi-scale local features for diabetic retinopathy grading

inferred← citeshttps://doi.org/10.1038/s41598-025-07561-xopenalex-snapshot

Transformer attention fusion for fine grained medical image classification

inferred← citeshttps://doi.org/10.1038/s41598-025-14944-7openalex-snapshot

Integrating non-linear radon transformation for diabetic retinopathy grading

inferred← citeshttps://doi.org/10.1038/s41598-025-25399-1openalex-snapshot

Task optimized vision transformer for diabetic retinopathy detection and classification in resource constrained early diagnosis settings

inferred← citeshttps://doi.org/10.1038/s41598-025-89262-zopenalex-snapshot

A novel approach for the segmentation of retinal images by integration K-means clustering algorithm with graph cut for image segmentation

inferred← citeshttps://doi.org/10.1038/s41598-025-97686-wopenalex-snapshot

Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification

inferred← citeshttps://doi.org/10.1038/s43587-024-00657-5openalex-snapshot

Deep learning-based prediction of one-year mortality in Finland is an accurate but unfair aging marker

inferred← citeshttps://doi.org/10.1049/cit2.12155openalex-snapshot

A deep convolutional neural network for diabetic retinopathy detection via mining local and long‐range dependence

inferred← citeshttps://doi.org/10.1049/cit2.12246openalex-snapshot

Mixed‐decomposed convolutional network: A lightweight yet efficient convolutional neural network for ocular disease recognition

inferred← citeshttps://doi.org/10.1049/cvi2.12116openalex-snapshot

Deep learning in the grading of diabetic retinopathy: A review

inferred← citeshttps://doi.org/10.1049/cvi2.12308openalex-snapshot

Enhancing semi‐supervised contrastive learning through saliency map for diabetic retinopathy grading

showing the first 200 of 450

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