Dual‐Scale Transformer Fusion With Meta Learning for Micro Metastasis Detection in Thyroid Cancer

Accurate detection of lymph node micrometastases is essential for thyroid cancer management but remains challenging due to their tiny size and limited annotated data. We propose MTFuse, a meta‐learning‐based dual‐scale transformer framework that integrates paired 10× and 4× patches to simultaneously capture cellular details and tissue context. Evaluated on the thyroid cancer micro‐metastasis dataset dataset, MTFuse achieved 98. 46% AUC, 93. 63% weighted recall, and 93. 77% weighted precision for micrometastasis detection, outperforming all state‐of‐the‐art convolutional neural network and transformer baselines. It also demonstrated strong robustness on macrometastases (AUC 99. 51%) and generalized effectively in 0‐shot settings to isolated tumor cells (AUC 93. 36%) and isolated psammoma bodies (AUC 91. 06%). These results show that MTFuse provides a highly accurate and interpretable solution for small‐lesion detection, offering strong potential for clinical deployment in data‐limited digital pathology workflows.