Deep Learning-Based Solvability of Underdetermined Inverse Problems in
Medical Imaging
- URL: http://arxiv.org/abs/2001.01432v3
- Date: Fri, 26 Jun 2020 00:17:55 GMT
- Title: Deep Learning-Based Solvability of Underdetermined Inverse Problems in
Medical Imaging
- Authors: Chang Min Hyun, Seong Hyeon Baek, Mingyu Lee, Sung Min Lee, and Jin
Keun Seo
- Abstract summary: This study focuses on learning the causal relationship regarding the structure of the training data suitable for deep learning, to solve highly underdetermined inverse problems.
We observe that a majority of the problems of solving underdetermined linear systems in medical imaging are highly non-linear.
Furthermore, we analyze if a desired reconstruction map can be learnable from the training data and underdetermined system.
- Score: 3.2214522506924093
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, with the significant developments in deep learning techniques,
solving underdetermined inverse problems has become one of the major concerns
in the medical imaging domain. Typical examples include undersampled magnetic
resonance imaging, interior tomography, and sparse-view computed tomography,
where deep learning techniques have achieved excellent performances. Although
deep learning methods appear to overcome the limitations of existing
mathematical methods when handling various underdetermined problems, there is a
lack of rigorous mathematical foundations that would allow us to elucidate the
reasons for the remarkable performance of deep learning methods. This study
focuses on learning the causal relationship regarding the structure of the
training data suitable for deep learning, to solve highly underdetermined
inverse problems. We observe that a majority of the problems of solving
underdetermined linear systems in medical imaging are highly non-linear.
Furthermore, we analyze if a desired reconstruction map can be learnable from
the training data and underdetermined system.
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