Medical Image Segmentation on BCV
88.55DSCPHTrans
Evaluation results
| Method | Links | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| PHTransPre-trained on ImageNet=false2022.03 | 88.55 | 8.68 | 92.54 | 80.89 | 85.25 | 91.3 | 97.04 | 83.42 | 91.2 | 86.75 | |
| nnU-NetPre-trained on ImageNet=false2022.03 | 87.75 | 9.83 | 92.83 | 80.66 | 84.86 | 89.78 | 97.17 | 82 | 92.39 | 82.31 | |
| nnFormerPre-trained on ImageNet=true2022.03 | 86.45 | 14.63 | 89.06 | 78.19 | 87.53 | 87.09 | 95.43 | 81.92 | 89.84 | 82.58 | |
| CoTrPre-trained on ImageNet=false2022.03 | 86.33 | 12.63 | 92.1 | 81.47 | 85.33 | 86.41 | 96.87 | 80.2 | 92.21 | 76.08 | |
| Swin UNETRPre-trained on ImageNet=false2022.03 | 85.78 | 17.75 | 92.78 | 76.55 | 86.25 | 89.12 | 96.91 | 77.22 | 88.7 | 79.72 | |
| MISSFormerPre-trained on ImageNet=false2022.03 | 81.96 | 18.2 | 86.99 | 68.65 | 85.21 | 82 | 94.41 | 65.67 | 91.92 | 80.81 | |
| UNETRPre-trained on ImageNet=false2022.03 | 79.42 | 29.27 | 88.92 | 69.8 | 81.38 | 79.71 | 94.28 | 58.93 | 86.14 | 76.22 | |
| Swin-UnetPre-trained on ImageNet=true2022.03 | 79.13 | 21.55 | 85.47 | 66.53 | 83.28 | 79.61 | 94.29 | 56.58 | 90.66 | 76.6 | |
| LeViT-UnetPre-trained on ImageNet=true2022.03 | 78.53 | 16.84 | 87.33 | 62.23 | 84.61 | 80.25 | 93.11 | 59.07 | 88.86 | 72.76 | |
| TransUNetPre-trained on ImageNet=true2022.03 | 77.48 | 31.69 | 87.23 | 63.13 | 81.87 | 77.02 | 94.08 | 55.86 | 85.08 | 75.62 |