Cross-Domain Generalization Neural Architecture Search for Robust Clinical Image Analysis

Abstract

In recent years, artificial intelligence (AI)-based clinical image analysis has achieved remarkable diagnostic performance; however, when it is practical deployment remains limited by poor generalization across heterogeneous clinical environments. Variations in imaging devices, acquisition protocols, patient demographics, and institutional practices frequently introduce domain shifts that substantially reduce the reliability of deep learning models when evaluated on previously unseen hospitals. This study proposes a Cross-Domain Generalization Neural Architecture Search (CDG-NAS) framework that automatically discovers neural network architectures capable of maintaining high diagnostic accuracy under diverse clinical domain shifts.
The proposed framework integrates domain generalization directly into the neural architecture search process by combining multi-domain architecture optimization, Domain Consistency Regularization (DCR), and a Robustness-Aware Architecture Scoring (RAS) strategy. During architecture search, candidate models are trained using multiple source domains while simultaneously being validated on unseen domains to encourage domain-invariant feature learning. Experiments were conducted using four publicly available clinical imaging datasets representing chest radiography, histopathology, brain magnetic resonance imaging, and retinal fundus imaging. The performance was evaluated using Accuracy, Precision, Recall, F1-score, Area Under the ROC Curve (AUC), Expected Calibration Error (ECE), Robustness Score, and paired statistical significance tests for the proposed framework.
The proposed CDG-NAS framework achieved an average cross-domain classification accuracy of 94.6%, outperforming conventional architectures including ResNet50 (85.7%), DenseNet121 (87.3%), EfficientNet-B4 (89.4%), Vision Transformer (90.2%), and Differentiable Architecture Search (91.1%). The framework improved the average robustness score by more than 8% under unseen domain conditions while simultaneously reducing inter-domain performance variance. Ablation experiments demonstrated that removing Domain Consistency Regularization reduced accuracy from 94.6% to 91.8%, whereas eliminating the Robustness-Aware Architecture Score decreased accuracy to 92.4%, confirming the contribution of each proposed component. Statistical analysis further verified that the observed improvements were significant (p < 0.01).
Unlike existing neural architecture search methods that optimize architectures primarily for source-domain accuracy, the proposed CDG-NAS framework incorporates domain robustness as a first-class optimization objective throughout the search process. The integration of multi-domain validation, Domain Consistency Regularization, and Robustness-Aware Architecture Scoring enables the automatic discovery of architectures that exhibit superior generalization across previously unseen clinical environments. This approach provides a practical and scalable solution for developing trustworthy clinical AI systems suitable for real-world deployment.