CBAM-enhanced CNN backbones reach a mean AUC of 0.8695 on the ChestXray14 dataset for imbalanced multi-label chest X-ray pathology classification, outperforming listed baselines.
Learning to Diagnose from Scratch by Exploiting Dependencies among Labels
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CBAM-DenseNet121 reaches 84.29% mean test accuracy on three-class chest X-ray classification with Grad-CAM visualizations showing plausible lung regions.
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Improving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbones
CBAM-enhanced CNN backbones reach a mean AUC of 0.8695 on the ChestXray14 dataset for imbalanced multi-label chest X-ray pathology classification, outperforming listed baselines.
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CBAM-Enhanced DenseNet121 for Multi-Class Chest X-Ray Classification with Grad-CAM Explainability
CBAM-DenseNet121 reaches 84.29% mean test accuracy on three-class chest X-ray classification with Grad-CAM visualizations showing plausible lung regions.