4.7 Article

Category weighted network and relation weighted label for diabetic retinopathy screening

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Endocrinology & Metabolism

IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045

Hong Sun et al.

Summary: This study provides global, regional, and country-level estimates of diabetes prevalence and health expenditures for 2021 and projections for 2045. The global prevalence of diabetes is increasing and is expected to have the greatest relative increase in middle-income countries. Diabetes-related health expenditures are also projected to rise.

DIABETES RESEARCH AND CLINICAL PRACTICE (2022)

Article Computer Science, Artificial Intelligence

Lesion-attention pyramid network for diabetic retinopathy grading

Xiang Li et al.

Summary: This paper presents a lesion-attention pyramid network (LAPN) for diabetic retinopathy (DR) diagnosis. By integrating subnetworks with different resolutions, LAPN can extract multi-scale features and use lesion activation maps as diagnostic evidence. Experimental results show that LAPN outperforms existing methods and provides additional evidence for clinical diagnosis.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2022)

Article Computer Science, Information Systems

Joint Learning of Multi-Level Tasks for Diabetic Retinopathy Grading on Low-Resolution Fundus Images

Xiaofei Wang et al.

Summary: This paper focuses on diabetic retinopathy (DR) grading using low-resolution fundus images. The study found that image super-resolution can improve the performance of lesion segmentation and DR grading, and the lesion segmentation regions of fundus images are highly consistent with pathological regions for DR grading.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Article Pathology

Predicting wet age-related macular degeneration (AMD) using DARC (detecting apoptosing retinal cells) AI (artificial intelligence) technology

Paolo Corazza et al.

Summary: The CNN DARC algorithm shows promising results in predicting new subretinal fluid formation in Age-related Macular Degeneration (AMD), with early DARC count significantly correlated to new SRF development. This demonstrates the potential of DARC as a biomarker for wet AMD, with further validation studies needed.

EXPERT REVIEW OF MOLECULAR DIAGNOSTICS (2021)

Article Computer Science, Interdisciplinary Applications

CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading

Along He et al.

Summary: The research suggests that attention modules effectively address the challenges of imbalanced data distribution and difficulty in identifying small lesions in diabetic retinopathy (DR) grading. By introducing category attention blocks and global attention blocks, the study has achieved significant results in DR grading.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

Article Computer Science, Interdisciplinary Applications

A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability

Yi Zhou et al.

Summary: Researchers have constructed a fine-grained annotated DR dataset to address the performance and interpretability issues faced by current DR diagnosis systems. This dataset contains two types of detailed annotated images, enabling in-depth studies on DR diagnosis.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

Review Ophthalmology

Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045 Systematic Review and Meta-analysis

Zhen Ling Teo et al.

Summary: The translated paragraphs provide updated estimates on the global prevalence and number of people with diabetic retinopathy (DR) through 2045. The burden of DR is expected to increase disproportionately in countries in the Middle East and North Africa and the Western Pacific by 2045. Certain ethnic groups such as Hispanics and Middle Easterners with diabetes are more likely to have DR compared to Asians.

OPHTHALMOLOGY (2021)

Article Chemistry, Analytical

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

Wejdan L. Alyoubi et al.

Summary: Diabetic retinopathy (DR) is a disease caused by diabetes complications, which can lead to blindness. This study introduces a fully automatic diagnosis system using deep learning models to accurately classify DR images and localize affected lesion areas. The combination of two models achieves a high accuracy rate of 89%.

SENSORS (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Influence-Balanced Loss for Imbalanced Visual Classification

Seulki Park et al.

Summary: This paper proposes a balancing training method with a new loss function that improves the performance of imbalanced data learning. Experimental results demonstrate the effectiveness of the proposed method and its superiority over state-of-the-art cost-sensitive loss methods.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (2021)

Article Computer Science, Information Systems

Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy

Sharmin Majumder et al.

Summary: A multitask deep learning model is proposed for more accurate detection of the five stages of DR, achieving good performance on two large datasets.

IEEE ACCESS (2021)

Article Computer Science, Information Systems

Multi-Scale Attention Network for Diabetic Retinopathy Classification

Mohammad T. Al-Antary et al.

Summary: This paper proposes the multi-scale attention network (MSA-Net) for DR classification by embedding retina images in high-level representational space, enriching the representation with mid and high-level features, describing retinal structure with a multi-scale feature pyramid, and enhancing discriminative power with a multi-scale attention mechanism. The model achieved outstanding results on EyePACS and APTOS datasets through training with cross-entropy loss for DR severity classification and weakly annotated data for healthy vs. non-healthy retina image detection.

IEEE ACCESS (2021)

Article Health Care Sciences & Services

Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis

Debasis Maji et al.

JOURNAL OF MEDICAL SYSTEMS (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Blood vessel detection from Retinal fundas images using GIFKCN classifier

Sambit S. Mondal et al.

INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE (2020)

Article Cardiac & Cardiovascular Systems

Estimating life years lost to diabetes: outcomes from analysis of National Diabetes Audit and Office of National Statistics data

Adrian H. Heald et al.

CARDIOVASCULAR ENDOCRINOLOGY & METABOLISM (2020)

Article Computer Science, Information Systems

Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening

Tao Li et al.

INFORMATION SCIENCES (2019)

Article Computer Science, Information Systems

Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images

U. Raghavendra et al.

INFORMATION SCIENCES (2018)

Article Computer Science, Artificial Intelligence

Deep image mining for diabetic retinopathy screening

Gwenole Quellec et al.

MEDICAL IMAGE ANALYSIS (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Densely Connected Convolutional Networks

Gao Huang et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Article Medicine, General & Internal

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

Varun Gulshan et al.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2016)

Article Materials Science, Multidisciplinary

FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE

Etienne Decenciere et al.

IMAGE ANALYSIS & STEREOLOGY (2014)

Article Ophthalmology

Automated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy

Michael D. Abramoff et al.

JAMA OPHTHALMOLOGY (2013)