# Convolutional block attention module

On nine-class chest radiograph classification, five lightweight attention or graph modules were each inserted into the same ResNet-50 backbone, and none beat the unmodified baseline across ten random seeds. The baseline reached a per-seed macro-AUC of 0.7990±0.0023; CBAM and GAT were significantly lower after multiple-comparison correction, yet every effect size fell inside a pre-specified 0.01-AUC equivalence margin. The result held after a 2.4-fold larger training set, on ResNet-18, EfficientNet-B0 and MobileNetV2, and under a patient-level split.

On nine-class chest radiograph classification, five lightweight attention or graph modules were each inserted into the same ResNet-50 backbone, and none beat the unmodified baseline across ten random seeds. The baseline reached a per-seed macro-AUC of 0.7990±0.0023; CBAM and GAT were significantly lower after multiple-comparison correction, yet every effect size fell inside a pre-specified 0.01-AUC equivalence margin. The result held after a 2.4-fold larger training set, on ResNet-18, EfficientNet-B0 and MobileNetV2, and under a patient-level split.