In healthcare applications, recent models for domain generalization (DG) are typically designed in collaborative settings across different institutions to eliminate the distinct covariates of each individual hospital. . Relevant techniques have been employed to construct domain generalization methods with a wider scope, including style-based data augmentations , episodic meta-learning strategies and domain-invariant feature learning using heuristic metrics or adversarial learning . Most of these methods limit the scope to CNN-based models and batch normalization architecture for image classification tasks.