The speed of data collection in many types of imaging technologies, including MRI, depends on the number of samples taken by the machine. When the number of collected samples is low, deep neural networks can be used to remove noise and the resulting visual artifacts.
The technology works. Very well. But there is no standard theoretical framework – no complete theory – to describe Why That works.
In one paper presented to the NeurIPS conference end of 2021, Ulugbek Kamilov, at the McKelvey School of Engineering at Washington University in St. Louis, and the co-authors paved the way to a clear framework. Kamilov is an assistant professor in the Department of Electrical and Systems Engineering at Preston M. Green and the Department of Computer Science and Engineering.
Kamilov’s findings prove, with some constraints, that an accurate image can be obtained by a deep neural network from very few samples if the image is of the type that can be represented by the network.
The finding is a starting point toward a solid understanding of why deep learning AI is able to produce accurate images, Kamilov said. It also has the potential to help determine the most efficient way to collect samples while obtaining an accurate image.
This research was supported by NSF Awards CCF-1813910, CCF-2043134, and CCF-204629 and the laboratory-led research and development program at Los Alamos National Laboratory under project #20200061DR.
The McKelvey School of Engineering at Washington University in St. Louis promotes independent research and education with an emphasis on scientific excellence, innovation, and collaboration without boundaries. McKelvey Engineering offers some of the best research and graduate programs in all departments, especially in biomedical engineering, environmental engineering, and computer science, and offers one of the most selective undergraduate programs in the country. With 140 full-time faculty, 1,387 undergraduate students, 1,448 graduate students, and 21,000 living alumni, we work to solve some of society’s greatest challenges; prepare students to become leaders and innovate throughout their careers; and to be a catalyst for economic development for the Saint-Louis region and beyond.
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