Selective multipathology chest X-ray classification via rejection mechanisms

Abstract Overconfidence in deep learning models poses a significant risk in high-stakes medical imaging tasks, particularly in multi-label classification of chest X-rays, where multiple co-occurring pathologies must be detected simultaneously. This study introduces an uncertainty-aware framework for chest X-ray diagnosis based on a DenseNet-121 backbone, enhanced with an entropy-based selective prediction mechanism that abstains, on a per-pathology basis, from predictions whose binary entropy exceeds a calibrated threshold, deferring those findings to clinical experts. A quantile-based calibration procedure tunes rejection thresholds using either global or class-specific strategies. On NIH ChestX-ray14, MIMIC-CXR, and PadChest, selective prediction is evaluated with risk-coverage analysis and balanced accuracy under non-parametric bootstrap confidence intervals. At a calibrated operating point, abstention improves balanced accuracy for all four target pathologies on NIH ChestX-ray14, with gains of 0.07–0.09 in balanced accuracy at a coverage of 0.70, a relative reduction in balanced error of 25–36% (95% confidence intervals excluding zero). Every target pathology is also supported on MIMIC-CXR and PadChest, on an independently trained model evaluated cross-dataset on a source excluded from its training, and under leave-one-dataset-out domain shift in both transfer directions. These results support the integration of selective prediction into AI-assisted diagnostic workflows, providing a practical step toward safer, uncertainty-aware deployment of deep learning in clinical settings.