Medical Image Segmentation on BraTS Challenge MSD (5-fold cross-validation train)
81.2Dice (ED)E2ENet
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| E2ENetFeature Sparsity (S)=0.7, Params (M)=11.24, FLOPs (G)=1067.062023.12 | 81.2 | 62.7 | 79.5 | 74.5 | 1.57 | |
| nnUNetParams (M)=31.2, FLOPs (G)=1076.622023.12 | 81 | 62 | 79.3 | 74.1 | 1.35 | |
| E2ENetFeature Sparsity (S)=0.8, Params (M)=9.44, FLOPs (G)=780.972023.12 | 81 | 62.5 | 79 | 74.2 | 1.72 | |
| E2ENetFeature Sparsity (S)=0.9, Params (M)=7.63, FLOPs (G)=494.522023.12 | 80.9 | 62.5 | 79.4 | 74.3 | 2 | |
| UNet++Params (M)=58.38, FLOPs (G)=3938.252023.12 | 80.5 | 62.5 | 79.2 | 74.1 | 1.12 | |
| DINTS2023.12 | 80.2 | 61.1 | 77.6 | 73 | — |