## 4 Experiments and Results
### Hydra Heads Performance
#### 4.2.2 Lung Segmentation  
To create a task-specific head for lung segmentation, the trained SISR model was truncated at the deepest convolutional layer and a symmetrical decoder was constructed and appended. Specifically, the SISR model (_Hydra_ trunk) was truncated at the sixth layer in Figure 2 and appended with the following layers: Conv2D (128) \(\rightarrow\) Conv2D (64) \(\rightarrow\) Conv2D (32) \(\rightarrow\) Conv2D (16) \(\rightarrow\) Conv2D (8) \(\rightarrow\) Conv2D (1). The segmentation head was trained to minimize the binary cross-entropy (BCE) loss. We used the weights of the SISR model to train the segmentation layers. We trained the segmentation layers using Adam optimizer, \(1\times 10^{-3}\) learning rate, and 200 epochs. As a baseline, we randomly initialized an individual model, which has the same encoder-decoder structure as the segmentation head, and optimized it for the search parameters. The baseline segmentation model has the following architecture: input (256 \(\times\) 256) \(\rightarrow\) Conv2D (8) \(\rightarrow\) Conv2D (16) \(\rightarrow\) Conv2D (32) \(\rightarrow\) Conv2D (64) \(\rightarrow\) Conv2D (128) \(\rightarrow\) Conv2D (256) \(\rightarrow\) Conv2D (128) \(\rightarrow\) Conv2D (64) \(\rightarrow\) Conv2D (32) \(\rightarrow\) Conv2D (16) \(\rightarrow\) Conv2D (8) \(\rightarrow\) Conv2D (1). We trained the baseline segmentation model to minimize the BCE loss. We performed two baseline experiments using the baseline segmentation model. In the first experiment, we used HR images as input to the baseline segmentation model. In the second experiment, we applied bi-cubic interpolation and used the interpolated images as input to the baseline segmentation model. Table 3 presents the performance of lung segmentation using the proposed approach and baseline model. Both models were trained on the Shenzhen dataset and evaluated on the Montgomery dataset. The performance was reported using the pixel accuracy, loss, and intersection over union (IoU). As shown in the table, the segmentation head, which uses the trunk as a backbone, outperformed baseline models in most scales. Figure 4 presents testing examples of the segmentation generated by the segmentation head as well as baseline approach and ground truth masks. The figure shows a clear visual improvement in the segmentation obtained by our proposed approach as compared to the traditional approach. These results suggest that initializing the segmentation head with the weights of the trunk enhances the overall performance. Recall that _Hydra_ head uses the trunk weights while the baseline model is initialized randomly. In addition to the Shenzhen and Montgomery datasets, we alternated between the Shenzhen, Montgomery, and JSRT datasets for training and testing to further evaluate the performance of the segmentation head. The testing sets of the Shenzhen and JRST datasets contain 100 and 140 images, respectively. Table 4 presents extensive evaluations of _Hydra_ segmentation head. These results suggest the excellent performance of the proposed approach in well-established datasets. Table 5 compares the performance and computation time for lung segmentation using the proposed _Hydra_, anatomical atlas method [40], and efficient U-Net [41]. As can be seen in the table, the proposed _Hydra_ outperforms similar approaches in the literature with the minimum computation time per image. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **IoU** \\ \hline Baseline (HR) & 0.9863 & 0.0355 & 0.9464 \\ \hline Baseline (scale 2) & 0.9700 & 0.0545 & 0.9263 \\ _Hydra_ Head (scale 2) & 0.9858 & 0.0316 & 0.9516 \\ \hline Baseline (scale 3) & 0.9613 & 0.0496 & 0.9285 \\ _Hydra_ Head (scale 3) & 0.9897 & 0.0248 & 0.9599 \\ \hline Baseline (scale 4) & 0.9390 & 0.0557 & 0.9201 \\ Hydra’s Head (scale 4) & 0.9801 & 0.0325 & 0.9229 \\ \hline Baseline (scale 8) & 0.9516 & 0.0816 & 0.8839 \\ _Hydra_ Head (scale 8) & 0.9830 & 0.0411 & 0.9391 \\ \hline \hline \end{tabular}
\end{table}