{"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/a-cascaded-multi-task-generative-framework","title":"A Cascaded Multi-Task Generative Framework for Detecting Aortic Dissection on 3D Non-contrast-enhanced Computed Tomography","arxiv_id":null,"date":"2022-10-01","proceeding":"journal 2022 10","authors":["Xiangyu Xiong","Yan Ding","Chuanqi Sun","Zhuoneng Zhang","Xiuhong Guan","Tianjing Zhang","Hao Chen","Hongyan Liu","Zhangbo Cheng","Lei Zhao","Xiaohai Ma","Guoxi Xie"],"abstract":"Contrast-enhanced computed tomography (CE-CT) is the gold standard for diagnosing aortic dissection (AD). However, contrast agents can cause allergic reactions or renal failure in some patients. Moreover, AD diagnosis by radiologists using non-contrast-enhanced CT (NCE-CT) images has poor sensitivity. To address this issue, we propose a novel cascaded multi-task generative framework for AD detection using NCE-CT volumes. The framework includes a 3D nnU-Net and a 3D multi-task generative architecture (3D MTGA). Specifically, the 3D nnU-Net was employed to segment aortas from NCE-CT volumes. The 3D MTGA was then employed to simultaneously synthesize CE-CT volumes, segment true & false lumen, and classify the patient as AD or non-AD. A theoretical formulation demonstrated that the 3D MTGA could increase the Jensen–Shannon Divergence (JSD) between AD and non-AD for each NCE-CT volume, thus indirectly improving the AD detection performance. Experiments also showed that the proposed framework could achieve an average accuracy of 0.831, a sensitivity of 0.938, and an F1-score of 0.847 in comparison with seven state-of-the-art classification models used by three radiologists with junior, intermediate, and senior experiences, respectively. The experimental results indicate that the proposed framework obtains superior performance to state-of-the-art models in AD detection. Thus, it has great potential to reduce the misdiagnosis of AD using NCE-CT in clinical practice.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9827558/authors#authors","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9827558","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":"a-cascaded-multi-task-generative-framework","repo_url":"https://github.com/yXiangXiong/CMTGF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"sensitivity","task_name":"Sensitivity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}