A two-step joint model based on deep learning realizes intelligent recognition of exfoliated cells in serous effusion.

Journal: Computational biology and chemistry
Published Date: Aug 9, 2025

Abstract

Cytological examination of serous effusion is critical for diagnosing malignancies, yet it heavily relies on subjective interpretation by pathologists, leading to inconsistent accuracy and misdiagnosis, especially in regions with limited medical resources. To address this challenge, we propose a two-step deep learning framework to standardize and enhance the diagnostic process. First, we improved the YOLOv8 model by integrating the Online Convolutional Reparameterization (OREPA) module, achieving a 93.09 % sensitivity for detecting abnormal cells. Second, we employed the Dual Attention Vision Transformer (DaViT) to classify normal cells (lymphocytes, mesothelial cells, histiocytes, neutrophils) with 98.74 % accuracy. By jointly deploying these models, our approach reduces missed diagnoses and provides granular insights into cell composition, offering a robust tool for rapid and objective cytopathological diagnosis. This work bridges the gap between AI-driven automation and clinical needs, particularly in resource-constrained settings.

Authors

  • Yige Yin
    School of Basic Medical Sciences, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
  • Xiaotao Li
    The College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.
  • Dongsheng Li
    Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Yue Hu
    Department of Biobank, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Qiang Wu
    Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, PR China; Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, PR China.
  • Jiarong Zhao
    Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China; Medical Pathology Center, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China.
  • Qiuyan Sun
    Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang, China; Key Laboratory of Cancer Etiology and Prevention, Liaoning Education Department, The First Hospital of China Medical University, Shenyang, China; Key Laboratory of GI Cancer Etiology and Prevention, Liaoning Province, The First Hospital of China Medical University, Shenyang, China.
  • Hong-Qiang Wang
    Biological Molecular Information System Laboratory, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
  • Wulin Yang
    School of Basic Medical Sciences, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China; Science Island Branch, Graduate School of University of Science and Technology of China, Hefei, China. Electronic address: yangw@cmpt.ac.cn.

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