Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation
This work addresses the challenge of simultaneously improving global and personalized models in distributed learning for medical imaging, which is incremental as it bridges a gap between existing method groups.
The paper tackles the problem of performance degradation in distributed learning for multi-center medical image segmentation due to data distribution variations, proposing a framework that decouples predictions for generic and local data, which demonstrated superior performance on both types of data in left atrial MRI segmentation.
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