  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
