SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation Article Swipe
Zahid Ullah
,
Muhammad Usman
,
Siddique Latif
,
Asifullah Khan
,
Jeonghwan Gwak
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-023-36311-0
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-023-36311-0
Diabetic retinopathy (DR) is a diabetes complication that can cause vision loss among patients due to damage to blood vessels in the retina. Early retinal screening can avoid the severe consequences of DR and enable timely treatment. Nowadays, researchers are trying to develop automated deep learning-based DR segmentation tools using retinal fundus images to help Ophthalmologists with DR screening and early diagnosis. However, recent studies are unable to design accurate models due to the unavailability of larger training data with consistent and fine-grained annotations. To address this problem, we propose a semi-supervised multitask learning approach that exploits widely available unlabelled data (i.e., Kaggle-EyePACS) to improve DR segmentation performance. The proposed model consists of novel multi-decoder architecture and involves both unsupervised and supervised learning phases. The model is trained for the unsupervised auxiliary task to effectively learn from additional unlabelled data and improve the performance of the primary task of DR segmentation. The proposed technique is rigorously evaluated on two publicly available datasets (i.e., FGADR and IDRiD) and results show that the proposed technique not only outperforms existing state-of-the-art techniques but also exhibits improved generalisation and robustness for cross-data evaluation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-023-36311-0
- https://www.nature.com/articles/s41598-023-36311-0.pdf
- OA Status
- gold
- Cited By
- 32
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379384705
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4379384705Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-023-36311-0Digital Object Identifier
- Title
-
SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-05Full publication date if available
- Authors
-
Zahid Ullah, Muhammad Usman, Siddique Latif, Asifullah Khan, Jeonghwan GwakList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-023-36311-0Publisher landing page
- PDF URL
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https://www.nature.com/articles/s41598-023-36311-0.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-023-36311-0.pdfDirect OA link when available
- Concepts
-
Diabetic retinopathy, Computer science, Task (project management), Segmentation, Artificial intelligence, Machine learning, Medicine, Diabetes mellitus, Endocrinology, Economics, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
32Total citation count in OpenAlex
- Citations by year (recent)
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2025: 11, 2024: 18, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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59Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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