Knee KL Grading on Knee KL Dataset (test)
0.9017QWKAGE-Net
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| AGE-NetVariant=Full2026.01 | 0.9017 | 0.2349 | 78.39 | 80.12 | 79.19 | |
| AGE-NetVariant=w/o Rank2026.01 | 0.8963 | 0.26 | 77.38 | 78.93 | 77.97 | |
| AGE-NetVariant=w/o AGR2026.01 | 0.8932 | 0.2665 | 76.82 | 77.86 | 76.34 | |
| AGE-NetVariant=Base ConvNeXt2026.01 | 0.8909 | 0.2795 | 77.01 | 77.74 | 76.32 | |
| VGG16Backbone=VGG162026.01 | 0.8654 | 0.3046 | 71.54 | 71.8 | 69.55 | |
| EfficientNetBackbone=EfficientNet2026.01 | 0.8638 | 0.3078 | 70.87 | 71.66 | 69.39 | |
| ConvNeXt-BaseBackbone=ConvNeXt-Base2026.01 | 0.8602 | 0.3248 | 71.32 | 72.95 | 72.98 | |
| Inception-V3Backbone=Inception-V32026.01 | 0.8475 | 0.3252 | 67.52 | 68.55 | 65.9 | |
| DenseNet121Backbone=DenseNet1212026.01 | 0.8427 | 0.3274 | 66.29 | 60.1 | 57.5 | |
| ResNet50Backbone=ResNet502026.01 | 0.8271 | 0.3711 | 62.72 | 59.32 | 56.29 | |
| Swin-TBackbone=Swin-T2026.01 | 0.5603 | 0.7767 | 35.31 | 29.35 | 33.63 | |
| ViT-BBackbone=ViT-B2026.01 | 0.3473 | 1.0413 | 26.82 | 16.12 | 25.19 |