Comparative analysis of convolutional neural networks and transformer architectures for breast cancer histopathological image classification

Our findings suggest that deep learning models are highly effective in classifying breast cancer pathology images, particularly in binary tasks where multiple models reach near-perfect performance. Although recent Transformer-based foundation models such as UNI possess strong feature extraction capabilities, their zero-shot performance on this specific task was limited. However, with simple fine-tuning, they quickly achieved excellent results. This indicates that with minimal adaptation, foundation models can be valuable tools in digital pathology, especially in complex multi-class scenarios.