  Performance degradation due to distribution discrepancy is a longstanding
challenge in intelligent imaging, particularly for chest X-rays (CXRs). Recent
studies have demonstrated that CNNs are biased toward styles (e.g.,
uninformative textures) rather than content (e.g., shape), in stark contrast to
the human vision system. Radiologists tend to learn visual cues from CXRs and
thus perform well across multiple domains. Motivated by this, we employ the
novel on-the-fly style randomization modules at both image (SRM-IL) and feature
(SRM-FL) levels to create rich style perturbed features while keeping the
content intact for robust cross-domain performance. Previous methods simulate
unseen domains by constructing new styles via interpolation or swapping styles
from existing data, limiting them to available source domains during training.
However, SRM-IL samples the style statistics from the possible value range of a
CXR image instead of the training data to achieve more diversified
augmentations. Moreover, we utilize pixel-wise learnable parameters in the
SRM-FL compared to pre-defined channel-wise mean and standard deviations as
style embeddings for capturing more representative style features.
Additionally, we leverage consistency regularizations on global semantic
features and predictive distributions from with and without style-perturbed
versions of the same CXR to tweak the model's sensitivity toward content
markers for accurate predictions. Our proposed method, trained on CheXpert and
MIMIC-CXR datasets, achieves 77.32$\pm$0.35, 88.38$\pm$0.19, 82.63$\pm$0.13
AUCs(%) on the unseen domain test datasets, i.e., BRAX, VinDr-CXR, and NIH
chest X-ray14, respectively, compared to 75.56$\pm$0.80, 87.57$\pm$0.46,
82.07$\pm$0.19 from state-of-the-art models on five-fold cross-validation with
statistically significant results in thoracic disease classification.
