CARE-Net: Causality-Aware Self-Supervised Learning with Anatomy-Constrained Attention for Robust Pneumonia Classification

Source abstract

Deep learning methods for pneumonia detection from chest X-ray images have achieved promising results; however, their reliability is limited by scarce labelled data, susceptibility to spurious correlations, poor cross-domain generalisation, and inadequate interpretability. This study proposes CARE-Net, a unified framework integrating radiology-aware Self-Supervised Learning (SSL), Causality-Aware Training (CAT), and Anatomy-Constrained Causality-Aware Attention (ACCA). Radiology-aware SSL learns robust representations from labelled and unlabelled chest X-ray images, while the Invariant Pneumonia Feature Loss (IPFL) reduces dependence on environment-specific information. ACCA further constrains model attention toward anatomically relevant lung regions to improve explanation alignment. Experimental evaluation on the RSNA and Chest X-ray Pneumonia datasets demonstrated strong classification performance. On the Chest X-ray Pneumonia dataset, CARE-Net achieved mean accuracy of 97.9 ± 0.7%, precision of 97.2 ± 0.8%, recall of 97.6 ± 0.7%, F1-score of 97.8 ± 0.6%, and AUC of 0.978 ± 0.005. On RSNA, it achieved accuracy of 95.6 ± 0.6%, AUC of 0.972 ± 0.004, and F1-score of 0.957 ± 0.005. The framework reduced performance variability by over 50%, improved robustness under distribution shift, and achieved lung-region IoU of 0.47 versus 0.32 for Grad-CAM. These findings demonstrate the potential of integrating SSL, causality-aware training, and anatomy-constrained attention for robust and interpretable pneumonia classification.