Q-CaMIR: Uncertainty-Aware Hybrid Quantum–Classical Learning for robust Cross-Modal radiological image classification

Achieving reliable radiological image classification requires not only high accuracy, but also well-calibrated uncertainty estimates, robustness in the face of domain shifts, and generalizability across various imaging modalities. Current deep learning methodologies for medical image classification frequently display overconfident predictions and experience significant degradation under realistic clinical conditions, such as label noise, limited training data, and distribution shifts between institutions. Hybrid quantum–classical machine learning has emerged as a promising paradigm; however, existing studies in medical imaging predominantly focus on single-modality and accuracy-centric approaches. In this study, we introduced Q-CaMIR (Cross-Modal, Uncertainty-Aware Hybrid Quantum–Classical Learning), a novel framework that integrates chest X-ray and brain MRI classification within a unified hybrid quantum–classical architecture, incorporating explicit uncertainty quantification. Q-CaMIR utilizes a pretrained classical vision backbone to extract modality-agnostic latent representations, aligns cross-modal features through a contrastive representation module, and channels the resulting compact embeddings through a variational quantum circuit (VQC) decision head, enhanced with evidential deep learning for uncertainty-aware prediction. We evaluated Q-CaMIR on three public benchmarks: CheXpert and MIMIC-CXR (external validation) for chest radiograph classification, and BraTS 2021 for brain tumor classification, employing a comprehensive suite of metrics including accuracy, macro-F1, AUROC, AUPRC, expected calibration error (ECE), and Brier score. Extensive experiments under low-data regimes, synthetic label noise, intensity corruption, and cross-institutional domain shifts demonstrate that Q-CaMIR achieves competitive classification performance compared to robust classical baselines while significantly improving calibration (up to 47% ECE reduction) and robustness. Ablation studies confirm the complementary contributions of the quantum decision head and uncertainty-aware training objective. Our findings suggest that hybrid quantum–classical learning provides a meaningful inductive bias for uncertainty-sensitive radiological classification, advancing towards clinically deployable AI systems for radiation-related diagnostic workflows.