{"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/mts-net-dual-enhanced-positional-multi-head","title":"MTS-Net: Dual-Enhanced Positional Multi-Head Self-Attention for 3D CT Diagnosis of May-Thurner Syndrome","arxiv_id":"2406.04680","date":"2024-06-07","proceeding":null,"authors":["Yixin Huang","Yiqi Jin","Ke Tao","Kaijian Xia","Jianfeng Gu","Lei Yu","Lan Du","Cunjian Chen"],"abstract":"May-Thurner Syndrome (MTS), also known as iliac vein compression syndrome or Cockett's syndrome, is a condition potentially impacting over 20 percent of the population, leading to an increased risk of iliofemoral deep venous thrombosis. In this paper, we present a 3D-based deep learning approach called MTS-Net for diagnosing May-Thurner Syndrome using CT scans. To effectively capture the spatial-temporal relationship among CT scans and emulate the clinical process of diagnosing MTS, we propose a novel attention module called the dual-enhanced positional multi-head self-attention (DEP-MHSA). The proposed DEP-MHSA reconsiders the role of positional embedding and incorporates a dual-enhanced positional embedding in both attention weights and residual connections. Further, we establish a new dataset, termed MTS-CT, consisting of 747 subjects. Experimental results demonstrate that our proposed approach achieves state-of-the-art MTS diagnosis results, and our self-attention design facilitates the spatial-temporal modeling. We believe that our DEP-MHSA is more suitable to handle CT image sequence modeling and the proposed dataset enables future research on MTS diagnosis. We make our code and dataset publicly available at: https://github.com/Nutingnon/MTS_dep_mhsa.","url_abs":"https://arxiv.org/abs/2406.04680v1","url_pdf":"https://arxiv.org/pdf/2406.04680v1.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":"mts-net-dual-enhanced-positional-multi-head","repo_url":"https://github.com/nutingnon/mts_dep_mhsa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"mts","method_name":"MTS"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}