A review of breast cancer histopathology image analysis with deep learning: Challenges, innovations, and clinical integration

Breast cancer (BC) is the most frequently diagnosed cancer among women and a leading cause of cancer-related mortality globally. Accurate and timely diagnosis is essential for improving patient outcomes. However, traditional histopathological assessments are labor-intensive and subjective, leading to inter-observer variability and diagnostic inconsistencies, especially in resource-limited settings. Furthermore, variability in tissue staining, limited availability of standardized annotated datasets, and subtle morphological patterns complicate the consistent characterization of tumors. Deep learning (DL) has recently emerged as a transformative technology in breast cancer pathology, providing automated and objective solutions for cancer detection, classification, and segmentation from histopathological images. This review systematically evaluates advanced deep learning (DL) architectures, including convolutional neural networks (CNNs), generative adversarial networks (GANs), autoencoders, deep belief networks (DBNs), extreme learning machines (ELMs), and transformer-based models such as Vision Transformers (ViTs) as well as transfer learning, attention-based explainable AI techniques, and multimodal integration to address these diagnostic challenges. Analyzing 199 references, including 182 peer-reviewed studies published between 2014 and 2025 and 17 reputable online sources (websites, databases, etc.), we identify key innovations, limitations, and opportunities for future research. Furthermore, we explore the critical roles of synthetic data augmentation, explainable AI (XAI), and multimodal integration to enhance clinical trust, model interpretability, and diagnostic precision, ultimately facilitating personalized and efficient patient care.