Transformer optimization with meta learning on pathology images for breast cancer lymph node micrometastasis.

Journal: NPJ digital medicine
Published Date: Jul 9, 2025

Abstract

Lymph node micro-metastasis represents the initial stage of breast cancer spread or metastasis. However, the limited size of these hidden lesions restricts dataset expansion, presenting a significant challenge for manual examination and conventional deep learning techniques. By harnessing the power of meta-learning on limited datasets, we developed a novel network named MetaTrans, equipped with a 34-category dataset (MT-MCD) to effectively pinpoint micro-metastases in lymph nodes from pathological images. MetaTrans demonstrated superior performance on two different multi-center datasets and excelled in the 0-shot task for intraoperative frozen section diagnosis. Beyond breast cancer, MetaTrans efficiently identifies micro-metastases in thyroid and colorectal cancers and can be directly applied to recognize images captured by digital cameras under a microscope. Across all clinical validation scenarios, our method surpasses state-of-the-art baselines, exhibiting robust cross-domain adaptation and task-specific reliability, which highlight its translational potential in diverse pathological settings.

Authors

  • Jing Huang
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Jingtao Wang
  • Junhai Shi
    Department of Pathology, Soochow Medical College, Soochow University, Suzhou, China.
  • Hengli Ni
    Department of Pathology, Children's Hospital, Soochow University, Suzhou, China.
  • Shan Xu
    Department of Intensive Care Unit, First Affiliated Hospital, Chongqing Medical University, Chongqing 400016, China.
  • Ping Wu
  • Yuexiang Ren
    Department of Pathology and Institute of Molecular Pathology, Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
  • Lijuan Bian
    Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Chenhan Su
    Department of Pathology, Soochow Medical College, Soochow University, Suzhou, China.
  • Yuxuan Xu
    The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
  • Xinyu He
  • Xinjian Chen
    Medical Image Processing, Analysis, and Visualization (MIVAP) Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, China.
  • Jianming Li
    Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China.

Keywords

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