文章：

三维乳腺癌图像分类的联邦学习架构

Federated Learning Architecture for 3D Breast Cancer Image Classification

原文发布日期：28 October 2025

DOI: 10.3390/cancers17213450

类型: Article

开放获取: 是

英文摘要：

Backgrouds: Breast cancer remains a major global health challenge, with early diagnosis playing a crucial role in improving patient survival rates. Among the available diagnostic techniques, mammography is widely employed for early detection. However, its effectiveness is often constrained by the complexity of image interpretation, which makes automated detection methods increasingly vital. Methods: In this study, we propose an advanced approach that leverages 3D mammographic imaging and integrates Federated Learning (FL) to enable decentralized, privacy-preserving model training across multiple institutions. To evaluate the effectiveness of this approach, we assess various machine learning models, including Convolutional Neural Networks (CNNs), Transfer Learning architectures (VGG16, VGG19, ResNet50), and AutoEncoders (AEs), using 3D mammographic data. Results: Our results indicate that the CNN model achieves an accuracy of 97.30%, which improves slightly to 97.37% when the model is combined with Federated Learning, highlighting both the predictive performance and privacy-preserving advantages of our method. In contrast, Transfer Learning models and AutoEncoders exhibit lower accuracies that range from 48.83% to 89.24%, revealing their limitations in the context of this specific task. Conclusions: These findings underscore the effectiveness of the CNN-FL framework as a robust tool for breast cancer detection, showing that this approach offers a promising balance between diagnostic accuracy and data security—two critical factors in medical imaging.

摘要翻译： 

背景：乳腺癌仍是全球重大健康挑战，早期诊断对提高患者生存率至关重要。在现有诊断技术中，乳腺X线摄影被广泛应用于早期筛查，但其诊断效能常受限于影像解读的复杂性，这使得自动化检测方法显得日益重要。方法：本研究提出一种创新方案，通过融合三维乳腺影像与联邦学习技术，实现跨医疗机构的去中心化隐私保护模型训练。为评估该方法效能，我们采用三维乳腺影像数据对多种机器学习模型进行测试，包括卷积神经网络、迁移学习架构（VGG16、VGG19、ResNet50）及自编码器。结果：实验数据显示，卷积神经网络模型准确率达97.30%，结合联邦学习后微升至97.37%，既展现了模型的预测性能，也凸显了隐私保护优势。相较之下，迁移学习模型与自编码器的准确率介于48.83%至89.24%之间，显示出在该特定任务中的局限性。结论：本研究证实CNN-FL框架可作为乳腺癌检测的有效工具，在诊断准确性与数据安全性这两个医学影像关键要素之间实现了良好平衡。

原文链接：

Federated Learning Architecture for 3D Breast Cancer Image Classification