Uncertainty‐suppressed long‐tailed learning with lightweight feature adaptation for multi‐label chest X‐ray classification

Abstract Background Report‐mined chest radiograph datasets commonly contain both long‐tailed label distributions and uncertain annotations. Conventional frequency‐based re‐weighting can strengthen rare‐label learning, but it may also amplify unreliable supervision when uncertain labels occur in minority classes. Purpose To develop and evaluate an uncertainty‐suppressed long‐tailed learning method with lightweight feature adaptation for multi‐label chest X‐ray (CXR) classification under imbalanced and uncertain report‐derived supervision. Methods The proposed uncertainty‐suppressed long‐tailed feature adaptation (USLFA) method combines a lightweight label‐adaptive feature head with an uncertainty‐suppressed long‐tailed loss. The primary analysis used a patient‐level 70%/10%/20% split of CheXpert for 14 observation labels. Robustness was further examined across two additional patient‐level partitions, on the recommended MIMIC‐CXR‐JPG test split without retraining, and on the official radiologist‐annotated CheXpert test set. Performance was assessed using macro‐AUROC, macro‐AUPRC, macro F1, frequency‐group analysis, ablation studies, and uncertainty‐handling comparisons. Ninety‐five percent confidence intervals (CIs) were estimated by patient‐level bootstrap resampling, and formal pairwise tests in the primary CheXpert analysis used paired permutation testing with Holm correction. Results On the primary CheXpert split, USLFA achieved a macro‐AUROC of 0.864 (95% CI, 0.856–0.871), macro‐AUPRC of 0.547 (95% CI, 0.532–0.560), and macro F1 of 0.501 (95% CI, 0.487–0.514). Compared with ConvNeXt‐Tiny using binary cross‐entropy (BCE), the gains in macro‐AUPRC and macro F1 were both 0.051 and remained significant after Holm correction (both p < 0.001). Compared with label‐distribution‐aware margin (LDAM), USLFA improved macro‐AUPRC by 0.018 (Holm‐adjusted p = 0.024) and macro F1 by 0.017 (Holm‐adjusted p = 0.041). The ordering was preserved across three patient‐level partitions, with USLFA macro‐AUPRC ranging from 0.540 to 0.555. On MIMIC‐CXR‐JPG, USLFA achieved a macro‐AUPRC of 0.485 (95% CI, 0.467–0.502), and on the official CheXpert test set it achieved a mean AUROC of 0.920 (95% CI, 0.908–0.931) across the five competition observations. Conclusions USLFA improved macro‐AUPRC and rare‐label recognition without introducing an additional inference‐time branch. Macro F1 was significantly higher than the principal BCE and LDAM baselines in the main comparison, whereas the additional macro F1 gain attributable to uncertainty suppression over U Soft was not significant after Holm correction. The repeated‐split, external‐dataset, and official‐test analyses provide complementary evidence that the observed advantage is not confined to a single custom partition.