Research Article
Study on Optimal Generative Network for Synthesizing Brain Tumor-Segmented MR Images
Table 2
Our discriminator’s and generator’s losses for each generative model: ResNet, and U-Net.
| Loss | ResNet | U-Net | Remarks |
| Dis | 0.430 | 4.890e‐3 | Sum of discriminator’s losses for A, B | DisA | 0.206 | 2.358e‐3 | Sum of discriminator’s losses for both real A and fake A | DisfakeA | 0.282 | 8.767e‐4 | Discriminator’s loss for fake A | DisrealA | 0.131 | 3.840e‐3 | Discriminator’s loss for real A | DisB | 0.223 | 2.532e‐3 | Sum of discriminator’s losses for both real B and fake B | DisfakeB | 0.252 | 3.000e‐3 | Discriminator’s loss for fake B | DisrealB | 0.195 | 1.027e‐3 | Discriminator’s loss for real B | Gen | 1.338 | 2.572 | Sum of generator’s losses for A ⟶ B, B ⟶ A | GenA ⟶ B | 1.185 | 1.591 | Generator’s loss for A ⟶ B | GenB ⟶ A | 1.125 | 1.549 | Generator’s loss for B ⟶ A |
|
|
A is the brain tumor domain and B is the segmentation mask domain.
|