用联邦学习实现多中心胰腺MRI精准分类，保护隐私同时保持高精度。
IPMN Risk Assessment under Federated Learning Paradigm
- 基于联邦学习框架，在多中心数据上训练模型，避免数据集中。
- 在652例T1和655例T2 MRI上，准确率接近集中式学习。
- 适合医疗多中心协作，尤其关注数据隐私的医学影像研究者。
准确分类胰管乳头状黏液性肿瘤（IPMN）对识别高风险病例至关重要。本研究构建了一个联邦学习框架，用于多中心IPMN分类，基于包含652例T1加权和655例T2加权MRI图像的综合性胰腺MRI数据集，该数据集来自7家顶尖医疗机构，是迄今最大且最多样化的IPMN分类数据集。我们评估了DenseNet-121在集中式与联邦设置下的表现。结果表明，联邦学习方法在保证机构间数据隐私的同时，达到了与集中式学习相当的分类精度。该工作标志着多中心协同IPMN分类的重要进展，实现了跨中心安全高效的高精度模型训练。
原文摘要 · Abstract (English)
Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset. This dataset includes 652 T1-weighted and 655 T2-weighted MRI images, accompanied by corresponding IPMN risk scores from 7 leading medical institutions, making it the largest and most diverse dataset for IPMN classification to date. We assess the performance of DenseNet-121 in both centralized and federated settings for training on distributed data. Our results demonstrate that the federated learning approach achieves high classification accuracy comparable to centralized learning while ensuring data privacy across institutions. This work marks a significant advancement in collaborative IPMN classification, facilitating secure and high-accuracy model training across multiple centers.
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