DenseNet model incorporating hybrid attention mechanisms and clinical features for pancreatic cystic tumor classification.

Journal: Journal of applied clinical medical physics
PMID: 38715381

Abstract

PURPOSE: The aim of this study is to develop a deep learning model capable of discriminating between pancreatic plasma cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) by leveraging patient-specific clinical features and imaging outcomes. The intent is to offer valuable diagnostic support to clinicians in their clinical decision-making processes.

Authors

  • Hui Tian
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
  • Bo Zhang
    Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China.
  • Zhiwei Zhang
    Department of Statistics, University of California, Riverside, California.
  • Zhenshun Xu
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
  • Liang Jin
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.
  • Yun Bian
    Department of Radiology, Changhai Hospital.
  • Jie Wu
    Center of Disease Control of Qingdao, 175 Shandong Road, Qingdao, Shandong, 266001, China.

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