STAR-ViT: A Spatially Transformed and Adversarially Realigned Vision Transformer for Pathogen Classification in Pediatric Pneumonia

Accurate computer-aided diagnosis of pediatric pneumonia remains challenging due to limited annotated data. To systematically address this challenge, a spatially transformed and adversarially realigned vision transformer (STAR-ViT) is proposed. STAR-ViT integrates spatial transformation consistency and adversarial cross-modal realignment within a single architecture. The spatial transformation module enforces feature consistency under random perturbations to enhance translation robustness, while the adversarial realignment module employs a domain discriminator to extract modality-invariant features and achieve implicit alignment between CT and X-ray images, thereby improving cross-modal generalization. During STAR-ViT training, a task-coordinated modulation (TCM) module is incorporated to stabilize multi-objective encoder optimization by dynamically adjusting task-specific optimization strengths on the shared encoder. Besides, to explicitly separate disease-related features from confounding variations, a causal feature modeling component is introduced, enabling more reliable and semantically meaningful representation learning. Experimental results show that STAR-ViT achieves an accuracy of 92.07% on a private CT dataset for pediatric pneumonia pathogen classification and 97.86% on the public ChestXRay2017 dataset for normal, bacterial pneumonia, and viral pneumonia classification, outperforming representative state-of-the-art methods.