ArticleScientific reports2023
Using DUCK-Net for polyp image 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 25 papers.
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Who cites it
25 citing papers in PubMed.
- Hard exudates in diabetic macular edema after intravitreal anti-VEGF therapy: a post-hoc analysis of the DRCR protocol T trial.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Trial
- Multimodal attention-enhanced network of segmenting acute ischemic stroke from perfusion images.Physical and engineering sciences in medicine · 2026Article
- Comparative Performance Evaluation of Federated and Centralized Learning for Velum and OTE Segmentation in Sleep Endoscopy Images.Journal of imaging informatics in medicine · 2026Article
- [Lung nodule segmentation method based on multiscale feature interaction and coordinate information].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- A feasibility study of deep learning-based segmentation of the inferior alveolar nerve on magnetic resonance neurography.Scientific reports · 2026Article
- A Machine learning pipeline to investigate tissue ingrowth in cerebral aneurysms using preclinical animal models.Scientific reports · 2026Article
- AI-Based Pulmonary Embolism Detection: The Added Value of a False-Positive Reduction Module over a Region Proposal Network.Diagnostics (Basel, Switzerland) · 2026Article
- MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- TransCat: a hybrid CNN-transformer network with KAN for medical image segmentation.Frontiers in bioinformatics · 2026Article
- CAFrontiers in artificial intelligence · 2026Article
- Multi-scale aggregation network for colonoscopic polyp segmentation via frequency domain decoupling.Scientific reports · 2025Article
- Diagnostic performance of real-time artificial intelligence using deep learning analysis of endoscopic ultrasound videos for gallbladder polypoid lesions.Scientific reports · 2025Article
- Marker-Less Lung Tumor Tracking from Real-Time Color X-Ray Fluoroscopic Images Using Cross-Patient Deep Learning Model.Bioengineering (Basel, Switzerland) · 2025Article
- Deep Q-Learning for Gastrointestinal Disease Detection and Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Deep learning based retinal hard exudates quantification of optical coherence tomography.International journal of retina and vitreous · 2025Article
- ColonNeXt: Fully Convolutional Attention for Polyp Segmentation.Journal of imaging informatics in medicine · 2025Article
- Adapting Classification Neural Network Architectures for Medical Image Segmentation Using Explainable AI.Journal of imaging · 2025Article
- CXR-Seg: A Novel Deep Learning Network for Lung Segmentation from Chest X-Ray Images.Bioengineering (Basel, Switzerland) · 2025Article
- MGB-Unet: An Improved Multiscale Unet with Bottleneck Transformer for Myositis Segmentation from Ultrasound Images.Journal of imaging informatics in medicine · 2025Article
- A Robust Blood Vessel Segmentation Technique for Angiographic Images Employing Multi-Scale Filtering Approach.Journal of clinical medicine · 2025Article
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3 authors.
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Abstract
This paper presents a novel supervised convolutional neural network architecture, "DUCK-Net", capable of effectively learning and generalizing from small amounts of medical images to perform accurate segmentation tasks. Our model utilizes an encoder-decoder structure with a residual downsampling mechanism and a custom convolutional block to capture and process image information at multiple resolutions in the encoder segment. We employ data augmentation techniques to enrich the training set, thus increasing our model's performance. While our architecture is versatile and applicable to various segmentation tasks, in this study, we demonstrate its capabilities specifically for polyp segmentation in colonoscopy images. We evaluate the performance of our method on several popular benchmark datasets for polyp segmentation, Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, and ETIS-LARIBPOLYPDB showing that it achieves state-of-the-art results in terms of mean Dice coefficient, Jaccard index, Precision, Recall, and Accuracy. Our approach demonstrates strong generalization capabilities, achieving excellent performance even with limited training data.
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