Managing Diabetic Retinopathy with Deep Learning: A Data Centric Overview
Summary: arXiv:2604.02448v1 Announce Type: cross
Abstract: Diabetic Retinopathy (DR) is a serious microvascular complication of diabetes and one of the leading causes of vision loss worldwide. Although automated detection and grading, with Deep Learning (DL), can reduce the burden on ophthalmologists, it is constrained by the limited availability of high-quality datasets. Existing repositories often remain geographically narrow, contain limited samples, and exhibit inconsistent annotations or variable image quality; thereby, restricting their clinical reliability.
This paper presents a comprehensive review and comparative analysis of fundus image datasets used in the management of DR. The study evaluates their usability across key tasks, including:
- Binary classification
- Severity grading
- Lesion localization
- Multi-disease screening
Additionally, the datasets are categorized by size, accessibility, and annotation type, such as:
- Image-level annotations
- Lesion-level annotations
- Multi-disease annotations
Furthermore, the paper highlights a recently published dataset as a case study to illustrate broader challenges in dataset curation and usage. This review consolidates current knowledge while pinpointing persistent gaps, such as:
- The lack of standardized lesion-level annotations
- The absence of longitudinal data
In discussing these challenges, the authors outline recommendations for future dataset development aimed at supporting clinically reliable and explainable solutions in DR screening. By improving the quality of datasets, the potential for enhanced detection and grading of diabetic retinopathy is significantly increased, which could lead to better patient outcomes and reduced vision loss.
Moreover, the integration of deep learning techniques in the analysis of fundus images not only streamlines the workflow for ophthalmologists but also ensures that patients receive timely and accurate diagnoses. The authors emphasize the importance of collaboration between researchers, clinicians, and data scientists to create a robust framework for dataset creation and validation.
In conclusion, while deep learning offers promising avenues for managing diabetic retinopathy, the success of these approaches heavily relies on the availability of high-quality, well-annotated datasets. Addressing the current limitations in dataset quality and accessibility will be crucial for advancing the field and improving clinical practices.