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DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images
Open accessAbstract
Diabetic retinopathy (DR) is a complication of diabetes that severely affects eyes. It can be graded into five levels of severity according to international protocol. However, optimizing a grading model to have strong generalizability requires a large amount of balanced training data, which is difficult to collect, particularly for the high severity levels. Typical data augmentation methods, including random flipping and rotation, cannot generate data with high diversity. In this paper, we propose a diabetic retinopathy generative adversarial network (DR-GAN) to synthesize high-resolution fundus images which can be manipulated with arbitrary grading and lesion information. Thus, large-scale generated data can be used for more meaningful augmentation to train a DR grading and lesion segmentation model. The proposed retina generator is conditioned on the structural and lesion masks, as well as adaptive grading vectors sampled from the latent grading space, which can be adopted to control the synthesized grading severity. Moreover, a multi-scale spatial and channel attention module is devised to improve the generation ability to synthesize small details. Multi-scale discriminators are designed to operate from large to small receptive fields, and joint adversarial losses are adopted to optimize the whole network in an end-to-end manner. With extensive experiments evaluated on the EyePACS dataset connected to Kaggle, as well as the FGADR dataset, we validate the effectiveness of our method, which can both synthesize highly realistic ( 1280 ×1280) controllable fundus images and contribute to the DR grading task.
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“the applied GAN architecture fails to synthesize some lesions, such as microaneurysms”
From Medical Image Data Augmentation: Techniques, Comparisons and Interpretations (2023) · cites this paper as Zhou et al. (2020)
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“the lesion and structural masks used as inputs are not real ground truth images”
From Medical Image Data Augmentation: Techniques, Comparisons and Interpretations (2023) · cites this paper as Zhou et al. (2020)
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“Therefore, the generator’s performance depends on the quality of those masks.”
From Medical Image Data Augmentation: Techniques, Comparisons and Interpretations (2023) · cites this paper as Zhou et al. (2020)
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Metadata
- Authors
- Zhou, Y., Wang, B., He, X., Cui, S., & Shao, L.
- Affiliations
- Southeast University (CN)
- Inception Institute of Artificial Intelligence (AE)
- ORCID
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2022-01
- Volume
- 26
- Issue
- 1
- Pages
- 56-66
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2998240002
- Cited by
- 155
- References
- 45
- Citations by year
- 2026: 18 · 2025: 37 · 2024: 32 · 2023: 36 · 2022: 20
- FWCI
- 8.27
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Keywords
- diabetic retinopathy, conditional generative adversarial networks, diabetic retinopathy grading, data augmentation, lesion segmentation, multi-scale attention, multi-scale discriminator, EyePACS
- MeSH terms
- Diabetes Mellitus, Diabetic Retinopathy, Fundus Oculi, Humans, Image Processing, Computer-Assisted, Retina
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