{"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/kdas3-knowledge-distillation-via-attention","title":"KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation","arxiv_id":"2312.08555","date":"2023-12-13","proceeding":null,"authors":["Quoc-Huy Trinh","Minh-Van Nguyen","Phuoc-Thao Vo Thi"],"abstract":"Polyp segmentation, a contentious issue in medical imaging, has seen numerous proposed methods aimed at improving the quality of segmented masks. While current state-of-the-art techniques yield impressive results, the size and computational cost of these models create challenges for practical industry applications. To address this challenge, we present KDAS, a Knowledge Distillation framework that incorporates attention supervision, and our proposed Symmetrical Guiding Module. This framework is designed to facilitate a compact student model with fewer parameters, allowing it to learn the strengths of the teacher model and mitigate the inconsistency between teacher features and student features, a common challenge in Knowledge Distillation, via the Symmetrical Guiding Module. Through extensive experiments, our compact models demonstrate their strength by achieving competitive results with state-of-the-art methods, offering a promising approach to creating compact models with high accuracy for polyp segmentation and in the medical imaging field. The implementation is available on https://github.com/huyquoctrinh/KDAS.","url_abs":"https://arxiv.org/abs/2312.08555v3","url_pdf":"https://arxiv.org/pdf/2312.08555v3.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":"kdas3-knowledge-distillation-via-attention","repo_url":"https://github.com/huyquoctrinh/kdas","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"kdas3-knowledge-distillation-via-attention","repo_url":"https://github.com/huyquoctrinh/kdas3","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"polyp-segmentation","task_name":"Polyp Segmentation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"knowledge-distillation","method_name":"Knowledge Distillation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/medical-image-segmentation-on-cvc-clinicdb","task":"Medical Image Segmentation","dataset":"CVC-ClinicDB","model":"KDAS","rank_in_archive_order":33,"of":48,"metrics":{"mIoU":"0.872","mean Dice":"0.925"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-cvc-colondb","task":"Medical Image Segmentation","dataset":"CVC-ColonDB","model":"KDAS","rank_in_archive_order":20,"of":25,"metrics":{"Average MAE":"0.032","mIoU":"0.679","mean Dice":"0.759"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-kvasir-seg","task":"Medical Image Segmentation","dataset":"Kvasir-SEG","model":"KDAS","rank_in_archive_order":32,"of":58,"metrics":{"Average MAE":"0.027","mIoU":"0.848","mean Dice":"0.913"},"uses_additional_data":false},{"leaderboard":"/sota/polyp-segmentation-on-kvasir-seg","task":"Polyp Segmentation","dataset":"Kvasir-SEG","model":"KDAS","rank_in_archive_order":2,"of":8,"metrics":{"mDice":"0.913","mIoU":"0.848"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}