## 3 Results  
### Stress Map Prediction Accuracy  
This section assesses the precision of matching 2D cross-section images of arterial walls with their corresponding von Mises stress map. To enhance the dataset, we leverage the physics of the problem by applying horizontal and/or vertical flipping to both the input images and output maps. This four-fold data augmentation is accomplished through three flipping operations: horizontal flip, vertical flip, and a combination of horizontal and vertical flip, as demonstrated in Figure 5.  
After data augmentation, the training data consists of 4000 images of various spatial arrangements of calcium in the arterial wall and their corresponding von Mises stress maps for each model case (train: test 85-15%). Figure 6a,b displayed the actual (simulated via FEM) and predicted von Mises stress maps using U-Net and cGAN. In Figure 6a, a test data image is presented, followed by a comparison between the real stress map, calculated using FEM, and those generated by U-Net and cGAN. CGAN provides a superior prediction of von Mises stress compared to U-Net, achieving an SSIM of 0.837 and a mean error percentage of 6.80, whereas U-Net attains an SSIM of 0.831 and a mean error percentage of 8.3. Figure 6b compares the stress map predictions made by the basic U-Net model and cGAN and the best U-Net and cGAN models obtained through ensemble techniques. The results for all models, including those using ensemble methods, are presented in Table 3. According to the table, the best ensemble model for U-Net was achieved by combining different U-Net architectures, as previously described. In this study, we utilized one U-Net model with 8 layers and another with 10 layers (by adding a layer with 32 channels at the beginning and end of the networks). The ensemble of these networks yielded the best results for U-Net, with a mean SSIM of 0.854. For cGAN, we applied the same technique to the generator layers and then combined it with a basic cGAN. The ensemble of two architectures also resulted in the best performance for cGAN (mean SSIM of 0.890). We then selected the best models and compared their predictions to those of the basic U-Net model and cGAN in Figure 6b. The ensemble technique for cGAN yielded superior predictions compared to the other models in this specific case, with an SSIM of 0.888.  
Notably, the deep learning approach significantly reduces computational effort compared to FE results. While Abaqus requires approximately 2 min for a single analysis, our trained U-Net model predicts stress on a laptop CPU in less than a second. Our deep learning model has been trained on a GPU, specifically the NVIDIA GEFORCE RTX3090, within a Jupyter notebook environment. The utilization of this GPU significantly enhances computational speed compared to relying solely on a CPU. When running on GPU, each analysis takes approximately 0.06 s to complete.  
Figure 5: 4-fold data augmentation by image flipping.  
Figure 6: [PERSON] stress map predicted from U-Net and cGAN architecture is based on 1000 FEM analyses of 2D arterial wall images, augmented to 4000 training images, (**a**) Test data image with true stress map (FEM) compared to U-Net and cGAN-generated maps. cGAN outperforms U-Net with SSIM 0.837 and 6.80% mean error. In comparison, U-Net has SSIM 0.831 and 8.3% mean error, (**b**) Comparison of stress map predictions between the best ensemble models and the basic U-Net and cGAN models, highlighting the superior performance of the ensemble cGAN (SSIM = 0.888).