Medical Image Classification on SynapseMNIST3D
0.88AUCAC-Base
Evaluation results
| Method | Links | ||
|---|---|---|---|
| AC-Base# of trainable params=427M2026.02 | 0.88 | 86.1 | |
| CdTransformer + CcCL2026.02 | 0.879 | 83.2 | |
| AC-Small# of trainable params=216M2026.02 | 0.875 | 84.1 | |
| ResNet-50+3D2026.02 | 0.851 | 79.5 | |
| AC-Tiny# of trainable params=9.5M2026.02 | 0.846 | 83.2 | |
| ResNet-18+3D2026.02 | 0.82 | 74.5 | |
| Official# of trainable params=33M2026.02 | 0.82 | 74.5 | |
| G-LoG bi-filtration (MLP)sigma=0.52026.02 | 0.81 | 82.7 | |
| G-LoG bi-filtration (MLP)sigma=12026.02 | 0.805 | 81.3 | |
| G-LoG bi-filtration (MLP)sigma=02026.02 | 0.783 | 79.6 | |
| G-LoG bi-filtration (MLP)sigma=1.52026.02 | 0.752 | 77.6 | |
| LMTTM-VMI# of trainable params=21M2026.02 | 0.737 | 77.7 | |
| ResNet-50+ACS2026.02 | 0.719 | 70.9 | |
| ResNet-18+ACS2026.02 | 0.705 | 72.2 | |
| ResNet-50+2.5D2026.02 | 0.669 | 73.5 | |
| Topo-Med (MLP)2026.02 | 0.653 | 48 | |
| ResNet-18+2.5D2026.02 | 0.634 | 69.6 | |
| auto-sklearn2026.02 | 0.631 | 73 | |
| AutoKeras2026.02 | 0.538 | 72.4 |