  Since data scarcity and data heterogeneity are prevailing for medical images,
well-trained Convolutional Neural Networks (CNNs) using previous normalization
methods may perform poorly when deployed to a new site. However, a reliable
model for real-world clinical applications should be able to generalize well
both on in-distribution (IND) and out-of-distribution (OOD) data (e.g., the new
site data). In this study, we present a novel normalization technique called
window normalization (WIN) to improve the model generalization on heterogeneous
medical images, which is a simple yet effective alternative to existing
normalization methods. Specifically, WIN perturbs the normalizing statistics
with the local statistics computed on the window of features. This
feature-level augmentation technique regularizes the models well and improves
their OOD generalization significantly. Taking its advantage, we propose a
novel self-distillation method called WIN-WIN for classification tasks. WIN-WIN
is easily implemented with twice forward passes and a consistency constraint,
which can be a simple extension for existing methods. Extensive experimental
results on various tasks (6 tasks) and datasets (24 datasets) demonstrate the
generality and effectiveness of our methods.
