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A review of breast cancer histopathology image analysis with deep learning: Challenges, innovations, and clinical integration
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DOI：10.1016/j.imavis.2025.105708.png)
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Abstract
En 中文
• Comprehensive analysis of 182 peer-reviewed studies on deep learning in breast cancer histopathology. • Evaluates CNNs, GANs, transformers, and multimodal models for classification, segmentation, and diagnosis. • Identifies major challenges including staining variability, dataset scarcity, and model interpretability. • Highlights the role of explainable AI (XAI) and synthetic data in enhancing model reliability and transparency. • Proposes future directions for clinical integration, fairness, and regulatory-compliant AI deployment.
Keywords:
Histopathology
Breast cancer
Deep learning
Detection
Diagnosis
Image analysis
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