Recent studies on enhancing breast cancer histopathology image analysis using neural architecture search, graph neural network, and generative adversarial networks

Deep learning has revolutionized histopathology image analysis in breast cancer, providing unprecedented accuracy in disease diagnosis and treatment planning. However, the manual design of neural network architectures for these tasks remains a significant challenge, often requiring extensive domain expertise and laborious experimentation. Our study investigates the advanced deep learning techniques such as Vision Transformers (ViTs), neural architecture search (NAS), graph neural networks (GNNs), and generative adversarial networks (GANs), on the analysis of breast cancer histopathology images. The advanced deep learning techniques surpass the performances of earlier traditional models by including the global context of histopathology images, and their structural relationships. Our study emphasize the importance of the transformative potential of these technologies and discusses their applications, benefits, and clinical validation considerations, specifying their growing importance in the domain of computational pathology. The emerging applications such as virtual staining (e.g., ViFIT), hybrid CNNViT architectures, and AI-driven quantitative scoring systems have shown significant promise in enhancing diagnostic precision, tissue characterization, and clinical workflow efficiency.