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.
Swinchex: Multi-label classification on chest x-ray images with transformers
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A new neural network stabilizes features for rare chest X-ray diseases via momentum anchoring and multi-scale fusion on EfficientNet, achieving 0.8682 AUC on ChestX-ray14.
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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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Momentum-Anchored Multi-Scale Fusion Model for Long-Tailed Chest X-Ray Classification
A new neural network stabilizes features for rare chest X-ray diseases via momentum anchoring and multi-scale fusion on EfficientNet, achieving 0.8682 AUC on ChestX-ray14.