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

作者：Jing Huang, Jingtao Wang, Junhai Shi, Hengli Ni, Shan Xu, Ping Wu, Yue Ren, Lijuan Bian, Chang Su, Yuxuan Xu, Xinyu He, Xinjian Chen, Jianming Li · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-025-01833-6 · 被引用次数：7 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Cutaneous Melanoma Detection and Management

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.