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Accelerating Super-Resolution and Visual Task Analysis in Medical Images.
Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with ﬁne structural details are preferred for visual task analysis. Recognizing this signiﬁcance, several deep learning networks have been proposed to enhance medical images for reliable automated interpretation. These deep networks are often computationally complex and require a massive number of parameters, which restrict them to highly capable computing platforms with large memory banks. In this paper, we propose an efﬁcient deep learning approach, called Hydra, which simultaneously reduces computational complexity and improves performance. The Hydra consists of a trunk and several computing heads. The trunk is a super-resolution model that learns the mapping from low-resolution to high-resolution images. It has a simple architecture that is trained using multiple scales at once to minimize a proposed learning-loss function. We also propose to append multiple task-speciﬁc heads to the trained Hydra trunkforsimultaneouslearningofmultiplevisualtasksinmedicalimages. TheHydraisevaluatedon publicly available chest X-ray image collections to perform image enhancement, lung segmentation, and abnormality classiﬁcation. Our experimental results support our claims and demonstrate that the proposed approach can improve the performance of super-resolution and visual task analysis in medical images at a remarkably reduced computational cost.