论文标题
模式银行：在不共享医疗数据的情况下学习跨数据中心的多模式图像
Modality Bank: Learn multi-modality images across data centers without sharing medical data
论文作者
论文摘要
多模式图像已被广泛使用，并为医学图像分析提供了全面的信息。但是，在临床环境中，所有机构中的所有方式都昂贵，而且通常是不可能的。为了利用更全面的多模式信息，我们提出了一个名为ModalityBank的隐私权分散的多模式自适应学习体系结构。我们的方法可以学习一组有效的特定域调制参数，插入了一个公共域 - 不合理的网络。我们通过切换不同的配置组来证明，生成器可以输出特定模式的高质量图像。我们的方法还可以完成所有数据中心的缺失方式，因此可以用于模态完成目的。从合成的多模式样本中训练的下游任务比从一个真实的数据中心学习并实现与所有真实图像相比，可以实现更高的性能。
Multi-modality images have been widely used and provide comprehensive information for medical image analysis. However, acquiring all modalities among all institutes is costly and often impossible in clinical settings. To leverage more comprehensive multi-modality information, we propose a privacy secured decentralized multi-modality adaptive learning architecture named ModalityBank. Our method could learn a set of effective domain-specific modulation parameters plugged into a common domain-agnostic network. We demonstrate by switching different sets of configurations, the generator could output high-quality images for a specific modality. Our method could also complete the missing modalities across all data centers, thus could be used for modality completion purposes. The downstream task trained from the synthesized multi-modality samples could achieve higher performance than learning from one real data center and achieve close-to-real performance compare with all real images.
