Classification network architecture | Augmentation | ROC-AUC (95% CI) | Sensitivity (%, 95% CI) | Specificity (%, 95% CI) | P-value |
---|---|---|---|---|---|
EfficientNetB0 | StyleGAN2 (Ours) | 0.926 (0.890–0.963) | 92.0 (82.4–97.3) | 80.8 (75.3–85.4) | Reference |
EfficientNetB0 | 0.825 (0.779–0.866) | 88.9 (78.4–95.4) | 65.2 (58.9–71.1) | 0.0048 | |
EfficientNetB0 | CutMixa [28] | 0.837 (0.792–0.877) | 77.8 (65.5–87.3) | 86.0 (81.1–90.1) | 0.0080 |
Vision Transformer [29] | Classic augmentationa | 0.835 (0.789–0.874) | 84.1 (72.7–92.1) | 75.2 (69.4–80.4) | 0.0051 |
Vision Transformer [29] | StyleGAN2 | 0.863 (0.819–0.899) | 82.5 (70.9–90.9) | 84.0 (78.9–88.3) | 0.0914 |