Multi-modal convolutional neural network-based thyroid cytology classification and diagnosis.

Journal: Human pathology
Published Date: Jul 4, 2025

Abstract

BACKGROUND: The cytologic diagnosis of thyroid nodules' benign and malignant nature based on cytological smears obtained through ultrasound-guided fine-needle aspiration is crucial for determining subsequent treatment plans. The development of artificial intelligence (AI) can assist pathologists in improving the efficiency and accuracy of cytological diagnoses. We propose a novel diagnostic model based on a network architecture that integrates cytologic images and digital ultrasound image features (CI-DUF) to solve the multi-class classification task of thyroid fine-needle aspiration cytology. We compare this model with a model relying solely on cytologic images (CI) and evaluate its performance and clinical application potential in thyroid cytology diagnosis.

Authors

  • Dandan Yang
    Center of Clinical Pharmacology, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, Zhejiang, China.
  • Tianlun Li
    The Department of Pathology, Affiliated Hospital of Jining Medical University, Jining, CHINA.
  • Lu Li
    State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei, China.
  • Shuai Chen
    State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
  • Xiangli Li
    The Institute of Technological Sciences, Wuhan University, Wuhan, CHINA.

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