论文标题
多中心左心房MRI分割的分布式学习中的分布式学习预测
Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation
论文作者
论文摘要
分布式学习在医学图像分析中表现出了巨大的潜力。它允许使用隐私保护的多中心培训数据。但是，由于不同的成像供应商和注释协议，本地中心的数据分布可能会彼此不同。这种差异降低了基于学习的方法的性能。为了减轻影响，已经提出了两组方法针对不同的目标，即全球方法和个性化方法。前者的目的是改善来自看不见的中心（称为通用数据）的所有测试数据的单个全局模型的性能；而后者则针对每个中心的多个模型（称为本地数据）。但是，几乎没有研究以同时实现这两个目标。在这项工作中，我们提出了一个新的分布式学习框架，该框架弥合了两组之间的差距，并提高了通用和本地数据的性能。具体而言，我们的方法通过分布条件的适应矩阵将通用数据和局部数据的预测分解。多中心左心房（LA）MRI分割的结果表明，我们的方法表明，在通用和局部数据上的现有方法表现出了优越的性能。我们的代码可从https://github.com/key1589745/decouple_predict获得
Distributed learning has shown great potential in medical image analysis. It allows to use multi-center training data with privacy protection. However, data distributions in local centers can vary from each other due to different imaging vendors, and annotation protocols. Such variation degrades the performance of learning-based methods. To mitigate the influence, two groups of methods have been proposed for different aims, i.e., the global methods and the personalized methods. The former are aimed to improve the performance of a single global model for all test data from unseen centers (known as generic data); while the latter target multiple models for each center (denoted as local data). However, little has been researched to achieve both goals simultaneously. In this work, we propose a new framework of distributed learning that bridges the gap between two groups, and improves the performance for both generic and local data. Specifically, our method decouples the predictions for generic data and local data, via distribution-conditioned adaptation matrices. Results on multi-center left atrial (LA) MRI segmentation showed that our method demonstrated superior performance over existing methods on both generic and local data. Our code is available at https://github.com/key1589745/decouple_predict
