## 4 Experiments and Results
### Results with the ACDC Dataset
#### 4.3.2 MYO and LVC Segmentation Stage  
Regarding the coarse segmentation substage, the same data augmentation operations used in the ROI extraction stage were applied in the current one, generating 10,386 slices to the EAIS-Net training set composed of ROIs extracted using the ground truth as a reference. Besides EAIS-Net, experiments with the conventional U-Net and a variation of this network containing the AIS blocks (Section 3.2.1) were carried out. These networks were trained with the following hyperparameters: 300 epochs, Jaccard Loss function, Adam optimizer, and a learning rate of 0.001 with a decay of 0.1. The Jaccard loss is a commonly used metric in semantic segmentation models, known for its scale invariance and its superior ability to reflect the perceptual quality of a model compared to pixel-wise accuracy [54; 55]. The choice of the Adam optimizer is based on findings in the literature in which this algorithm has shown better performance than stochastic optimization methods [50]. The learning rate, decay factor, and number of \begin{table}
\begin{tabular}{l c c c} \hline  & **Dice** & **IoU** \\ \hline
**U-Net A** & **0.9368** & **0.8820** \\ U-Net B & 0.9041 & 0.8261 \\ \hline \end{tabular}
\end{table} 
Table 1: ROI extraction stage: comparative results between U-Net A and B. \begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{4}{c}{**MAE Analysis**} \\ \cline{2-5}  & **mAP** & **MAE** & **St. Dev.** & **Max.** & **Min.** \\ \hline
**U-Net A** & **0.90** & **2.93** & **4.27** & **18.25** & **0.00** \\ U-Net B & 0.60 & 6.93 & 6.18 & 24.00 & 0.75 \\ \hline \end{tabular}
\end{table} 
Table 2: MAE and mAP results for evaluating the generated bounding boxes. epochs were chosen empirically. However, as the network has a high number of trainable parameters (about 53 million), a high number of training epochs is necessary for the model to converge [56]. In this case, the conventional U-Net is a basis for comparison as it is widely used in medical image segmentation methods. Its variation with AIS Blocks incorporated was tested to analyze the impact of using these structures. Table 3 shows the obtained results. The test set contains 386 slices. It comprises the ROIs extracted in the previous stage, emulating the proposed method's workflow. U-Net with AIS blocks in the decoder path produced better MYO segmentations than the conventional U-Net. For the LVC segmentation, there was a slight decrease in the Dice coefficient of 0.0027 and an increase in the IoU value of 0.0032. Based on these results, it is possible to validate the use of the proposed AIS blocks as a structure that can provide improvements. The EAIS-Net, in turn, presents results that are significantly different from the Dice and IoU values obtained by the other networks. This network uses the EfficientNet B3 as the backbone and AIS blocks in the decoder path. For the LVC segmentation, EAIS-Net produced Dice of 0.9154 and IoU of 0.8742, an average improvement of 2.9% over the conventional U-Net. For the MYO segmentation, these values are, respectively, 0.8454 and 0.7578. These results indicate an average improvement of 5% over the U-Net result. Compared to other EfficientNet models, it is possible to observe that the B5 and B7 models achieved lower results than the B3 model, which is part of the EAIS-Net. Therefore, based on this experiment, EAIS-Net was chosen to compose this stage of the proposed method. However, as mentioned in Section 3.2, there was a hypothesis that the results could be improved by including a second segmentation stage, which would be applied to the smaller ROIs. Therefore, to validate this hypothesis, tests were conducted with the same networks used in the previous experiment, which receive as input the new ROIs extracted by the mask aggregation process reported in Section 3.2.2. The networks were trained using a set of ROIs extracted by the mask aggregation process, using ground truth as reference segmentation.