Perineural Invasion (PNI) Classification on PNI R + S (50%)
0.767Mean AUCPattenNet
Evaluation results
| Method | Links | |
|---|---|---|
| PattenNetImplementation=3D, Validation=5-fold cross-validation2026.03 | 0.767 | |
| ResNet-50Implementation=3D, Validation=5-fold cross-validation2026.03 | 0.7599 | |
| EfficientNet-B3Implementation=3D, Validation=5-fold cross-validation2026.03 | 0.7462 | |
| SwinTransformerImplementation=3D, Validation=5-fold cross-validation2026.03 | 0.7454 | |
| ResNet-152Implementation=3D, Validation=5-fold cross-validation2026.03 | 0.7421 | |
| DenseNet-169Implementation=3D, Validation=5-fold cross-validation2026.03 | 0.7387 | |
| DenseNet-121Implementation=3D, Validation=5-fold cross-validation2026.03 | 0.738 | |
| ResNet-200Implementation=3D, Validation=5-fold cross-validation2026.03 | 0.7307 | |
| DenseNet-201Implementation=3D, Validation=5-fold cross-validation2026.03 | 0.7226 | |
| EfficientNet-B1Implementation=3D, Validation=5-fold cross-validation2026.03 | 0.7098 | |
| EfficientNet-B2Implementation=3D, Validation=5-fold cross-validation2026.03 | 0.7085 | |
| ResNet-101Implementation=3D, Validation=5-fold cross-validation2026.03 | 0.7024 | |
| DenseNet-264Implementation=3D, Validation=5-fold cross-validation2026.03 | 0.7012 | |
| EfficientNet-B0Implementation=3D, Validation=5-fold cross-validation2026.03 | 0.6995 |