Machine Learning Approach for Inverse Scattering Problem = 기계학습을 이용한 역산란 문제 연구

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Recently, deep learning has significantly extended its scope from classification and low-level computer vision problems to various inverse problems such as x-ray low-dose CT, compressed sensing MRI, etc. Beyond these linear inverse problems where an off-the-shelf neural network from computer vision suffices to remove image artifacts, can machine learning learn complex non-linear physics such as photon scattering in highly diffusive medium? Here we develop a novel deep learning approach that reversely traces the photon scattering through diffusive medium to obtain an accurate 3D distribution of optical anomalies. Based on the deep convolutional framelets theory, we show that each layer of the proposed neural network actually learns the mappings that directly inverts the Lippmann-Schwinger equation and the bases of a low dimensional signal manifold. The proposed framework showed a robust performance over the various examples of the inverse problems including diffuse optical tomography (DOT) and elastic imaging. Compared to the conventional model-based approaches, the proposed deep network has many advantages. First, the inversion of the Lippmann-Swinger equation is fully data-driven such that we do not need any explicit modeling of the acquisition system and boundary conditions. Second, unlike the explicit regularization in the model-based approaches, the low-dimensional signal manifold is embedded as convolutional layers that are also learned from the data. Third, unlike the conventional approach whose performance is fixed, the proposed deep learning approach benefits from a newly gathered data by exploiting new information. Fourth, after training the network, it can reconstruct the final image within a few hundred of milliseconds since the model does not need to be re-computed per each individual data. Moreover, since the network learns a new signal representation instead of a simple denoising kernel from images to images, which is common in the convolutional neural network applications in the computer vision area, the proposed neural network is felicitous to provide a robust reconstruction in the real data even though it is only trained using a generated set of numerical data.
Advisors
Ye, Jong Chulresearcher예종철researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2018.2,[vii, 118 p. :]

Keywords

inverse scattering problem▼adeep convolutional framelets▼adeep neural network▼amachine learning▼adeep learning▼adiffuse optical tomography▼aelastic imaging; 역산란 문제▼a깊은 컨볼루션 프레임렛▼a깊은 전산망▼a기계 학습▼a깊은 학습▼a산란광 단층 촬영▼a탄성촬영

URI
http://hdl.handle.net/10203/264692
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=734296&flag=dissertation
Appears in Collection
BiS-Theses_Ph.D.(박사논문)
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