{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/using-duck-net-for-polyp-image-segmentation-1","title":"Using DUCK-Net for Polyp Image Segmentation","arxiv_id":"2311.02239","date":"2023-11-03","proceeding":"Nature Scientific Reports 2023 6","authors":["Razvan-Gabriel Dumitru","Darius Peteleaza","Catalin Craciun"],"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. The code is publicly available on GitHub: https://github.com/RazvanDu/DUCK-Net","url_abs":"https://arxiv.org/abs/2311.02239v1","url_pdf":"https://arxiv.org/pdf/2311.02239v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"using-duck-net-for-polyp-image-segmentation-1","repo_url":"https://github.com/RazvanDu/DUCK-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"using-duck-net-for-polyp-image-segmentation-1","repo_url":"https://github.com/zh320/medical-segmentation-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/medical-image-segmentation-on-cvc-clinicdb","task":"Medical Image Segmentation","dataset":"CVC-ClinicDB","model":"DUCK-Net","rank_in_archive_order":11,"of":48,"metrics":{"mIoU":"0.9009","mean Dice":"0.9478"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-cvc-colondb","task":"Medical Image Segmentation","dataset":"CVC-ColonDB","model":"DUCK-Net","rank_in_archive_order":2,"of":25,"metrics":{"mIoU":"0.8785","mean Dice":"0.9353"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-etis","task":"Medical Image Segmentation","dataset":"ETIS-LARIBPOLYPDB","model":"DUCK-Net","rank_in_archive_order":3,"of":25,"metrics":{"mIoU":"0.8788","mean Dice":"0.9354"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-kvasir-seg","task":"Medical Image Segmentation","dataset":"Kvasir-SEG","model":"DUCK-Net","rank_in_archive_order":1,"of":58,"metrics":{"Precision":"0.9628","Recall":"0.9379","mIoU":"0.9051","mean Dice":"0.9502"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.02239","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}