Medical Image Classification on BUSI (test)
94.89AccuracyCPR
Evaluation results
| Method | Links | |
|---|---|---|
| CPRVenue=N/A, Mean Resolution=615×501, Mem. (G)=8.12026.07 | 94.89 | |
| Adaptive NNVenue=NMI’2025, Mean Resolution=615×501, Mem. (G)=8.12026.07 | 92.97 | |
| OverLoCKVenue=CVPR’2025, Mean Resolution=615×501, Mem. (G)=23.82026.07 | 92.97 | |
| GLNetVenue=NIPS’2024, Mean Resolution=615×501, Mem. (G)=12.42026.07 | 92.33 | |
| MGCAVenue=EAAI’2025, Mean Resolution=615×501, Mem. (G)=8.32026.07 | 92.01 | |
| HieraVenue=ICML’2023, Mean Resolution=615×501, Mem. (G)=8.12026.07 | 91.37 | |
| LKABackbone=Swin-L, #Trainable Parameters=0.652M2025.06 | 90.2 | |
| Full fine-tuningBackbone=Swin-L, #Trainable Parameters=195M2025.06 | 89.4 | |
| LOSSLESS ADAPTATIONBackbone=Swin-L, #Trainable Parameters=0.320M2025.06 | 88.7 | |
| LORABackbone=Swin-L, #Trainable Parameters=0.578M2025.06 | 88.7 | |
| AdapterformerBackbone=Swin-L, #Trainable Parameters=0.320M2025.06 | 88.4 | |
| ConvpassBackbone=Swin-L, #Trainable Parameters=0.661M2025.06 | 88.4 | |
| CIATBackbone=Swin-L, #Trainable Parameters=0.966M2025.06 | 88.4 | |
| BitfitBackbone=Swin-L, #Trainable Parameters=0.313M2025.06 | 88.1 | |
| RepAdapterBackbone=Swin-L, #Trainable Parameters=0.486M2025.06 | 88.1 | |
| Linear probingBackbone=Swin-L, #Trainable Parameters=0.006M2025.06 | 87.1 | |
| mammo-CLIPVenue=MICCAI’2024, Mean Resolution=615×501, Mem. (G)=10.52026.07 | 85.62 | |
| VPTBackbone=Swin-L, #Trainable Parameters=1.052M2025.06 | 85.2 | |
| GABMILVenue=MICCAI’2025, Mean Resolution=615×501, Mem. (G)=8.32026.07 | 84.66 | |
| AIMBackbone=Swin-L, #Trainable Parameters=0.947M2025.06 | 84.5 | |
| AdapterBackbone=Swin-L, #Trainable Parameters=0.633M2025.06 | 84.3 | |
| PAMILVenue=CVPR’2024, Mean Resolution=615×501, Mem. (G)=11.32026.07 | 83.07 | |
| ST-AdapterBackbone=Swin-L, #Trainable Parameters=0.334M2025.06 | 71.3 |