Medical Image Classification on Brain (test)
96.8AccuracyLKA
Evaluation Results
| Method | Links | |
|---|---|---|
| LKABackbone=Swin-L, #Trainable Parameters=0.652M2025.06 | 96.8 | |
| Full fine-tuningBackbone=Swin-L, #Trainable Parameters=195M2025.06 | 96.7 | |
| LOSSLESS ADAPTATIONBackbone=Swin-L, #Trainable Parameters=0.320M2025.06 | 95.2 | |
| RepAdapterBackbone=Swin-L, #Trainable Parameters=0.486M2025.06 | 95.2 | |
| LORABackbone=Swin-L, #Trainable Parameters=0.578M2025.06 | 95.2 | |
| CIATBackbone=Swin-L, #Trainable Parameters=0.966M2025.06 | 95.2 | |
| AdapterformerBackbone=Swin-L, #Trainable Parameters=0.320M2025.06 | 94.8 | |
| ConvpassBackbone=Swin-L, #Trainable Parameters=0.661M2025.06 | 93.3 | |
| VPTBackbone=Swin-L, #Trainable Parameters=1.052M2025.06 | 93.3 | |
| BitfitBackbone=Swin-L, #Trainable Parameters=0.313M2025.06 | 92 | |
| AdapterBackbone=Swin-L, #Trainable Parameters=0.633M2025.06 | 92 | |
| AIMBackbone=Swin-L, #Trainable Parameters=0.947M2025.06 | 87.7 | |
| ST-AdapterBackbone=Swin-L, #Trainable Parameters=0.334M2025.06 | 86.7 | |
| Linear probingBackbone=Swin-L, #Trainable Parameters=0.006M2025.06 | 82.2 |