Improved DeTraC Binary Coyote Net‐Based Multiple Instance Learning for Predicting Lymph Node Metastasis of Breast Cancer From Whole‐Slide Pathological Images
M. Ramkumar, R. Sarath Kumar, R. Padmapriya, S. Balu Mahandiran · International Journal of Medical Robotics and Computer Assisted Surgery · 2024
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.