Scholarlink

<p>Breast cancer (BC) is one of the most common malignancies among women and is associated with complex mechanisms in cell signalling pathways and tumour heterogeneity. These complexities have made the early screening and selection of effective treatments a significant challenge. Federated Learning (FL), a new approach in artificial intelligence, enables the analysis of multicenter data without requiring physical data aggregation. This feature leverages privacy-preserving mechanisms, such as secure aggregation, to improve model accuracy while preserving patient data confidentiality. In the field of BC, FL can significantly improve the accuracy of imaging-based diagnostic algorithms and the efficiency of personalized treatment methods by integrating data from multiple medical institutions. Despite these advantages, the application of FL in the field of BC is associated with specific challenges, including data heterogeneity across institutions, high computational and communication overhead caused by repeated local model training and iterative federated communication rounds, and difficulty in ensuring model generalizability in diverse clinical settings, which pose critical practical limitations for widespread clinical adoption. This review article systematically examines the applications of FL in BC. In the analysis of the articles, it was found that the most significant focus is on improving the accuracy and precision of results, Precision and Accuracy Enhancement (PAE) with 29%, followed by Data Confidentiality and Protection (DCP) with 24%, and finally Model Robustness and Generalization (MRG) with 19%. The findings of this study indicate that FL can improve screening accuracy, recommend more targeted treatments, and, at the same time, prevent the disclosure of sensitive patient information. Ultimately, this research can help oncology and data science researchers and experts better understand the potential and challenges of FL and develop new strategies to improve the quality of clinical care in BC.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The application of federated learning in breast cancer diagnosis and treatment: a survey

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月8日

查看全文
  • Huafeng Wu,
  • Mohsen Ghorbian

摘要

Breast cancer (BC) is one of the most common malignancies among women and is associated with complex mechanisms in cell signalling pathways and tumour heterogeneity. These complexities have made the early screening and selection of effective treatments a significant challenge. Federated Learning (FL), a new approach in artificial intelligence, enables the analysis of multicenter data without requiring physical data aggregation. This feature leverages privacy-preserving mechanisms, such as secure aggregation, to improve model accuracy while preserving patient data confidentiality. In the field of BC, FL can significantly improve the accuracy of imaging-based diagnostic algorithms and the efficiency of personalized treatment methods by integrating data from multiple medical institutions. Despite these advantages, the application of FL in the field of BC is associated with specific challenges, including data heterogeneity across institutions, high computational and communication overhead caused by repeated local model training and iterative federated communication rounds, and difficulty in ensuring model generalizability in diverse clinical settings, which pose critical practical limitations for widespread clinical adoption. This review article systematically examines the applications of FL in BC. In the analysis of the articles, it was found that the most significant focus is on improving the accuracy and precision of results, Precision and Accuracy Enhancement (PAE) with 29%, followed by Data Confidentiality and Protection (DCP) with 24%, and finally Model Robustness and Generalization (MRG) with 19%. The findings of this study indicate that FL can improve screening accuracy, recommend more targeted treatments, and, at the same time, prevent the disclosure of sensitive patient information. Ultimately, this research can help oncology and data science researchers and experts better understand the potential and challenges of FL and develop new strategies to improve the quality of clinical care in BC.