用于个性化乳腺癌预测的可信隐私保护多模态联邦学习
Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction
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中文总结 AI 辅助
研究探讨联邦学习能否兼顾四个关键支柱，支持乳腺癌患者肿瘤进展预测模型开发。通过多模态数据评估联邦学习框架，与集中式模型比较性能，并研究相关策略，结果助于理解隐私保护多模态预测建模可行性及支持未来应用。
中文摘要 AI 辅助
联邦学习已成为解决使用敏感健康数据训练预测模型时隐私问题的潜在方案，尤其在个性化癌症护理中。本研究探讨联邦学习能否在解决透明度、可扩展性、安全性和公平性这四个关键部署支柱的同时，支持开发用于预测乳腺癌患者肿瘤进展的稳健模型。研究评估了一个使用多模态数据（包括临床信息、肿瘤特征、生物标志物数据、患者人口统计学数据以及MRI扫描等医学影像数据）的联邦学习框架，以模拟肿瘤特征随时间的变化。将联邦方法的性能与在聚合数据上训练的集中式模型进行比较。报告还进一步研究了增强安全模型更新以及在患者亚组中保持性能和跨机构支持可扩展性的策略。研究结果评估了联邦学习在保护数据局部性的同时能否实现与集中式学习相当的预测性能。这些结果有助于理解隐私保护多模态预测建模的可行性，并支持未来如数字双胞胎等应用，以协助临床医生和患者进行个性化治疗规划。
英文摘要
Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models, particularly in personalised cancer care. This research investigates whether federated learning can support the development of robust models for predicting tumour progression in breast cancer patients while addressing four critical deployment pillars: transparency, scalability, security, and fairness. This study evaluates a federated learning framework using multimodal data, including clinical information, tumour characteristics, biomarker data, and patient demographics, alongside medical imaging data such as MRI scans, to model changes in tumour characteristics over time. The performance of the federated approach was compared with that of a centralised model trained on aggregated data. The report then further examines strategies to enhance secure model updates, maintain performance across patient subgroups, and support scalability across institutions. The findings assess whether federated learning can achieve predictive performance comparable to centralised learning while preserving data locality. These results contribute to understanding the feasibility of privacy-preserving, multimodal predictive modelling and support future applications such as digital twins to assist clinicians and patients in personalised treatment planning.
发表机构
- NTU（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。
