ArticleScientific reports2023
SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 42 citations in OpenAlex.
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- Brain CT image classification based on mask RCNN and attention mechanism.Scientific reports · 2024Article
- Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation.Scientific reports · 2024Article
- Improving privacy-preserving multi-faceted long short-term memory for accurate evaluation of encrypted time-series MRI images in heart disease.Scientific reports · 2024Article
- Performance enhancement of deep learning based solutions for pharyngeal airway space segmentation on MRI scans.Scientific reports · 2024Article
- Attention-enhanced dilated convolution for Parkinson's disease detection using transcranial sonography.Biomedical engineering online · 2024Article
- Mask-Transformer-Based Networks for Teeth Segmentation in Panoramic Radiographs.Bioengineering (Basel, Switzerland) · 2023Article
- Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification.Frontiers in bioengineering and biotechnology · 2023Article
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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