Medical Image Classification on VesselMNIST3D
0.959AUCCdTransformer + CcCL
Evaluation results
| Method | Links | ||
|---|---|---|---|
| CdTransformer + CcCL2026.02 | 0.959 | 92.9 | |
| G-LoG bi-filtration (MLP)sigma=1.52026.02 | 0.933 | 93.7 | |
| AC-Base# of trainable params=427M2026.02 | 0.933 | 95 | |
| ResNet-18+ACS2026.02 | 0.93 | 92.8 | |
| AC-Tiny# of trainable params=9.5M2026.02 | 0.928 | 95.5 | |
| BSDA# of trainable params=33M2026.02 | 0.917 | 93.2 | |
| LMTTM-VMI# of trainable params=21M2026.02 | 0.917 | 93.1 | |
| G-LoG bi-filtration (MLP)sigma=12026.02 | 0.915 | 90.8 | |
| ResNet-50+ACS2026.02 | 0.912 | 85.8 | |
| auto-sklearn2026.02 | 0.91 | 91.5 | |
| AC-Small# of trainable params=216M2026.02 | 0.909 | 94.5 | |
| ResNet-50+3D2026.02 | 0.907 | 91.8 | |
| G-LoG bi-filtration (MLP)sigma=0.52026.02 | 0.877 | 89.8 | |
| ResNet-18+3D2026.02 | 0.874 | 87.7 | |
| Official# of trainable params=33M2026.02 | 0.874 | 87.7 | |
| ViViT-M + R-LLM# of trainable params=295M2026.02 | 0.87 | 90.6 | |
| G-LoG bi-filtration (MLP)sigma=02026.02 | 0.813 | 88.7 | |
| Topo-Med (MLP)2026.02 | 0.808 | 73.6 | |
| AutoKeras2026.02 | 0.773 | 89.4 | |
| ResNet-50+2.5D2026.02 | 0.751 | 87.7 | |
| ResNet-18+2.5D2026.02 | 0.748 | 84.6 |