Deep Learning-Based Survival Prediction for Breast Cancer Using Whole Slide Histopathology Images
Breast cancer is currently one of the leading malignancies and mortality rates among women globally, creating an urgent need for accurate and efficient automated diagnostic tools. This study proposes and systematically compares convolutional neural network (CNN) architectures, including VG16, ResNet18, ResNet50, DenseNet121, EfficientNet-B0, and Swin-Transformer, applied to whole slide image (WSI) histopathology data for breast cancer prediction and classification. The models were trained and evaluated on the same WSI dataset with consistent image preprocessing techniques, allowing for objective comparison of performance. Experimental results showed that the performance of the models varied depending on the training strategy. In the non-fine-tune setting, ResNet18 achieved the best results with a C-index of 0.6587 and a Mean time-dependent AUC of 0.7317. When performing fine-tuning, ResNet50 outperformed the other architectures with a C-index of 0.6877 and a Mean AUC of 0.7738. Meanwhile, in the non-fine-tuned setup with combined clinical testing, Swin-Transformer achieved the highest performance with a C-index of 0.6793 and a Mean AUC of 0.7619.