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2019 | OriginalPaper | Chapter

Self-supervised Learning of Inverse Problem Solvers in Medical Imaging

Authors : Ortal Senouf, Sanketh Vedula, Tomer Weiss, Alex Bronstein, Oleg Michailovich, Michael Zibulevsky

Published in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Publisher: Springer International Publishing

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Abstract

In the past few years, deep learning-based methods have demonstrated enormous success for solving inverse problems in medical imaging. In this work, we address the following question: Given a set of measurements obtained from real imaging experiments, what is the best way to use a learnable model and the physics of the modality to solve the inverse problem and reconstruct the latent image? Standard supervised learning based methods approach this problem by collecting data sets of known latent images and their corresponding measurements. However, these methods are often impractical due to the lack of availability of appropriately sized training sets, and, more generally, due to the inherent difficulty in measuring the “groundtruth” latent image. In light of this, we propose a self-supervised approach to training inverse models in medical imaging in the absence of aligned data. Our method only requiring access to the measurements and the forward model at training. We showcase its effectiveness on inverse problems arising in accelerated magnetic resonance imaging (MRI).
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Metadata
Title
Self-supervised Learning of Inverse Problem Solvers in Medical Imaging
Authors
Ortal Senouf
Sanketh Vedula
Tomer Weiss
Alex Bronstein
Oleg Michailovich
Michael Zibulevsky
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_13

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