Accelerating Super-Resolution and Visual Task Analysis in Medical Images

Abstract

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 trunk for simultaneous learning of multiple visual tasks in medical images. The Hydra is evaluated on 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.

Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review

“…The possibilities and reliability of images recreated using deep learning methods are proven by the fact that they are utilized for the improvement of the resolution of medical imaging [179]- [181]. An example is a solution proposed by Zamzmi et al [182] that enables to enhance the resolution of X-ray images. The first stage of this algorithm consists in performing bicubic interpolation.…”

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification

“…In addition, there is a contradiction between bone suppression and super-resolution tasks. For the bone suppression task, it is better to use a skip connection to fuse the interpolated input and the convolved feature map so that the obtained HR CXR image can have better imaging quality (55). However, for the super-resolution task, the purpose is to suppress the bone signals in the original CXR images; retention of shallow feature information affects the final performance of bone suppression.…”

Süper Çözünürlük Yönteminin Uydu İmgelerinin Sınıflandırma Performansına Etkisi

“…In the medical field, SISR plays a vital role in improving the resolution of medical images, such as MRI or CT scans. HR images facilitate better diagnosis, analysis, and treatment planning, leading to improved patient care [54]. SISR is crucial for improving the resolution of satellite or aerial imagery used in various applications, including surveillance, mapping, and urban planning.…”