Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume
  Segmentation
Yongsong Huang Wanqing Xie Mingzhen Li Mingmei Cheng Jinzhou Wu Weixiao Wang Jane You Xiaofeng Liu
Federated learning (FL) enables multiple client medical institutes
collaboratively train a deep learning (DL) model with privacy protection.
However, the performance of FL can be constrained by the limited availability
of labeled data in small institutes and the heterogeneous (i.e., non-i.i.d.)
data distribution across institutes. Though data augmentation has been a proven
technique to boost the generalization capabilities of conventional centralized
DL as a "free lunch", its application in FL is largely underexplored. Notably,
constrained by costly labeling, 3D medical segmentation generally relies on
data augmentation. In this work, we aim to develop a vicinal feature-level data
augmentation (VFDA) scheme to efficiently alleviate the local feature shift and
facilitate collaborative training for privacy-aware FL segmentation. We take
both the inner- and inter-institute divergence into consideration, without the
need for cross-institute transfer of raw data or their mixup. Specifically, we
exploit the batch-wise feature statistics (e.g., mean and standard deviation)
in each institute to abstractly represent the discrepancy of data, and model
each feature statistic probabilistically via a Gaussian prototype, with the
mean corresponding to the original statistic and the variance quantifying the
augmentation scope. From the vicinal risk minimization perspective, novel
feature statistics can be drawn from the Gaussian distribution to fulfill
augmentation. The variance is explicitly derived by the data bias in each
individual institute and the underlying feature statistics characterized by all
participating institutes. The added-on VFDA consistently yielded marked
improvements over six advanced FL methods on both 3D brain tumor and cardiac
segmentation.