Multi-scale feature fusion with task-specific data synthesis for pneumonia pathogen classification

Pneumonia pathogen diagnosis from chest X-rays (CXR) is essential for timely and effective treatment for pediatric patients. However, the radiographic manifestations of pediatric pneumonia are often less distinct than those in adults, challenging for pathogen diagnosis, even for experienced clinicians. In this work, we propose a novel framework that integrates an adaptive hierarchical fusion network (AHFF) with task-specific diffusion-based data synthesis for pediatric pneumonia pathogen classification in clinical CXR. AHFF consists of dual branches to extract global and local features, and an adaptive feature fusion module that hierarchically integrates semantic information using cross attention mechanisms. Further, we develop a classifier-guided diffusion model that uses the task-specific AHFF classifier to generate class-consistent chest X-ray images for data augmentation. Experiments on one private and two public datasets demonstrate that the proposed classification model achieves superior performance, with accuracies of 78.00%, 84.43%, and 91.73%, respectively. Diffusion-based augmentation further improves accuracy to 84.37% using the private dataset. These results highlight the potential of feature fusion and data synthesis for improving automated pathogen-specific pneumonia diagnosis in clinical settings.