Improved DeTraC Binary Coyote Net‐Based Multiple Instance Learning for Predicting Lymph Node Metastasis of Breast Cancer From Whole‐Slide Pathological Images

ABSTRACT Background Early detection of lymph node metastasis in breast cancer is vital for improving treatment outcomes and prognosis. Methods This study introduces an Improved Decompose, Transfer, and Compose Binary Coyote Net‐based Multiple Instance Learning (ImDeTraC‐BCNet‐MIL) method for predicting lymph node metastasis from Whole Slide Images (WSIs) using multiple instance learning. The method involves segmenting WSIs into patches using Otsu and double‐dimensional clustering techniques. The developed multiple instance learning approach introduces a paradigm into computational pathology by shaping pathological data and constructing features. ImDeTraC‐BCNet‐MIL was utilised for feature generation during both training and testing to differentiate lymph node metastasis in WSIs. Results The proposed model achieves the highest accuracy of 95.3% and 99.8%, precision values of 98% and 99.8%, and recall rates of 92.9% and 99.8% on the Camelyon16 and Camelyon17 datasets. Conclusions These findings underscore the effectiveness of ImDeTraC‐BCNet‐MIL in enhancing the early detection of lymph node metastasis in breast cancer.