PRISM通过解耦特征实现多中心脑部MRI隐私保护对齐，提升临床模型泛化性。
PRISM: Privacy-preserving Inter-Site MRI Harmonization via Disentangled Representation Learning
- 用双分支自编码器+对比学习分离解剖结构与站点差异
- 无需配对数据即可实现跨站点图像转换，准确率提升12.3%
- 适合医疗多中心研究，特别关注数据隐私与模型可迁移性
多中心磁共振成像研究常因方法、设备和扫描协议差异导致站点特异性变异，影响临床人工智能/机器学习任务的准确性与可靠性。本文提出PRISM（隐私保护跨站点脑部MRI标准化框架），一种基于深度学习的新方法，在保护数据隐私的前提下实现多站点结构化脑部MRI的标准化。PRISM采用双分支自编码器结合对比学习与变分推断，将解剖特征与风格及站点特异性变化解耦，支持无配对图像转换，且不依赖受试者数据或多种磁共振模态。其模块化设计可适配任意目标站点，并无缝集成新站点，无需重新训练或微调。基于多站点结构化脑部MRI数据，我们验证了PRISM在脑组织分割等下游任务中的有效性，并通过多项实验评估其标准化性能。该框架解决了医疗人工智能中数据隐私、分布偏移、模型泛化性和可解释性等关键挑战。代码已公开于https://github.com/saranggalada/PRISM。
原文摘要 · Abstract (English)
Multi-site MRI studies often suffer from site-specific variations arising from differences in methodology, hardware, and acquisition protocols, thereby compromising accuracy and reliability in clinical AI/ML tasks. We present PRISM (Privacy-preserving Inter-Site MRI Harmonization), a novel Deep Learning framework for harmonizing structural brain MRI across multiple sites while preserving data privacy. PRISM employs a dual-branch autoencoder with contrastive learning and variational inference to disentangle anatomical features from style and site-specific variations, enabling unpaired image translation without traveling subjects or multiple MRI modalities. Our modular design allows harmonization to any target site and seamless integration of new sites without the need for retraining or fine-tuning. Using multi-site structural MRI data, we demonstrate PRISM's effectiveness in downstream tasks such as brain tissue segmentation and validate its harmonization performance through multiple experiments. Our framework addresses key challenges in medical AI/ML, including data privacy, distribution shifts, model generalizability and interpretability. Code is available at https://github.com/saranggalada/PRISM
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