A monotonic multi-expert Vision Transformer for clinically reliable chest X-ray classification

Chest X-ray (CXR)-based recognition of pulmonary diseases remains challenging due to overlapping radiographic patterns, class imbalance, and variability across imaging sources. These factors often lead to unstable performance in large-scale multi-class classification tasks.

In this study, pulmonary disease recognition is formulated as a 21-class primary-label classification benchmark using an integrated multi-source dataset constructed from five public chest X-ray repositories. Although some source datasets originally contain multi-label annotations, the final benchmark is reorganized into a primary-label format, where each image is assigned one target label from the unified 21-class label space. This formulation is used as a controlled benchmark simplification rather than a complete representation of real-world multi-label clinical diagnosis. We propose a structured QMIX-ViT multi-expert framework, where disease categories are decomposed into specialized groups modeled by individual Vision Transformer (ViT) experts. The expert outputs are fused through a QMIX-inspired monotonic mixing mechanism to support consistent global decision-making. The proposed model is evaluated against convolutional and Transformer-based baselines, including ResNet, DenseNet, CheXNet, EfficientNet, ViT-Tiny, and ViT-Base, using Precision, Recall, F1-score, AUROC, and AUPRC.

Experimental results show that the proposed framework achieves improved decision-level performance, particularly in Precision, Recall, and F1-score, under the constructed benchmark setting.

The results suggest that disease-group expert decomposition and monotonic fusion can reduce inter-class interference and improve decision-level stability. Overall, the proposed QMIX-ViT framework provides a structured approach for multi-class chest X-ray classification under heterogeneous data conditions.