## 5 Conclusions and Possible Extensions  
HR images with fine structural details are preferred for visual task analysis in medical images. Several deep learning networks have been proposed in the literature to enhance the resolution of medical images. However, these networks are very deep and require a massive number of parameters,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Task** & **Train Parameters** & **Train Time (Seconds)** & **Test Time (Seconds)** \\ \hline VDSR [21] & SISR & 668,227 & 72,490.26 & 0.23 \\ \hline \multirow{3}{*}{_Hydra_} & SISR (Trunk) & 395,717 & 21,917.64 & 0.09 \\  & Classification Head & 295,715 & 180.75 & 0.01 \\  & Segmentation Head & 786,497 & 387.87 & 0.03 \\ \hline \multirow{3}{*}{Traditional} & Baseline Classification & 1,573,506 & 650.72 & 0.05 \\  & Baseline Segmentation & 786,497 & 8238.98 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of training parameters and time for _Hydra_, VDSR, and the traditional approach.  
which restrict them to platforms with large memory banks and computing capability. To address this problem, we propose an efficient approach, called the _Hydra_, to accelerate SISR and visual task analysis in medical images. The _Hydra_ consists of a SISR trunk and task-specific heads. The trunk is a small SISR model that learns the mapping from LR to HR. The experimental results demonstrated that the performance of the _Hydra_ trunk is comparable to the state-of-the-art VDSR model. After constructing the trunk, task-specific heads are appended to learn task-specific features for the key visual tasks in medical images. The proposed _Hydra_ was evaluated on CXR datasets to perform image enhancement, abnormality classification, and lung segmentation. The experimental results demonstrated that the proposed approach outperformed baseline approach by a large margin. In addition, quantitative measurements of the _Hydra_ showed a remarkable reduction in terms of resources and computations as compared to the baseline traditional approach and methods in the literature.  
Although we only demonstrated the performance of our approach in constructing HR CXR images followed by classification and segmentation, the _Hydra_ can be extended in several ways. For example, instead of using a single LR image, multiple LR images can be used as input to the SISR model (_Hydra_ trunk) followed by fusing the reconstructed images as proposed in [44, 45] to generate a fused HR image. Besides, the proposed _Hydra_ can be extended to perform pre-processing operations in 3D images using 3D CNNs (e.g., 3D SISR [46]) as a shared trunk followed by appending the trained trunk with multiple heads for different 3D tasks. The _Hydra_ can also be extended and used for other medical image applications. For example, a trunk can be trained to construct HR CT images followed by appending the trunk with task-specific layers to perform different CT tasks. In another imaging modality, a _Hydra_ trunk can be trained to de-noise MRI images followed by simultaneous visual task analyses including the detection of a brain tumor, its location, and the segmentation of the tumor from the background.  
[PERSON], [PERSON] and [PERSON] designed the framework, [PERSON] and [PERSON] conceived the experiment(s), [PERSON] and [PERSON] conducted the experiment(s), [PERSON], [PERSON] and [PERSON] analyzed the results. [PERSON] wrote the manuscript. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Intramural Research Program of the National Library of Medicine (NLM) and the National Institutes of Health (NIH).  
The authors declare no conflict of interest.