SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation

Article


Ullah, Zahid, Akram, Muhammad, Latif, Siddique, Khan, Asifullah and Gwak, Jeonghwan. 2023. "SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation." Scientific Reports. 13 (1). https://doi.org/10.1038/s41598-023-36311-0
Article Title

SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation

ERA Journal ID201487
Article CategoryArticle
AuthorsUllah, Zahid, Akram, Muhammad, Latif, Siddique, Khan, Asifullah and Gwak, Jeonghwan
Journal TitleScientific Reports
Journal Citation13 (1)
Article Number9087
Number of Pages16
Year2023
PublisherNature Publishing Group
Place of PublicationUnited Kingdom
ISSN2045-2322
Digital Object Identifier (DOI)https://doi.org/10.1038/s41598-023-36311-0
Web Address (URL)https://www.nature.com/articles/s41598-023-36311-0
AbstractDiabetic 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.
KeywordsSSMD‑UNet; Diabetic retinopathy
ANZSRC Field of Research 20204206. Public health
Byline AffiliationsKorea National University of Transportation, South Korea
Seoul National University, Korea
University of Southern Queensland
Pakistan Institute of Engineering and Applied Sciences (PIEAS), Pakistan
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