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Methods inspired by Artificial Intelligence (AI) are starting to fundamentally change computational science and engineering through breakthrough performances on challenging problems.
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Some investigations on robustness of deep learning in limited angle tomography
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Biomedical image reconstruction: From the foundations to deep neural networks
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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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On instabilities of deep learning in image reconstruction and the potential costs of AI
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Robust compressed sensing using generative models
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Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
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Optimism in the face of adversity: Understanding and improving deep learning through adversarial robustness
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Evaluation and development of deep neural networks for image super-resolution in optical microscopy
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Deep learning for PET image reconstruction
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A mean-field variational inference approach to deep image prior for inverse problems in medical imaging
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Deep learning techniques for inverse problems in imaging
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Improving robustness of deep-learning-based image reconstruction
A. Raj, Y. Bresler, and B. Li · 2020
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Image reconstruction: From sparsity to data-adaptive methods and machine learning
S. Ravishankar, J. C. Ye, and J. A. Fessler · 2020
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End-to-end variational networks for accelerated MRI reconstruction
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Deep learning for tomographic image reconstruction
G. Wang, J. C. Ye, and B. De Man · 2020
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Deep neural networks are effective at learning high-dimensional Hilbert-valued functions from limited data
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E. Wu, K. Wu, R. Daneshjou, D. Ouyang, D. E. Ho, and J. Zou · 2021
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Instabilities in conventional multi-coil MRI reconstruction with small adversarial perturbations
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Localized adversarial artifacts for compressed sensing MRI
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P. Binev, A. Bonito, R. DeVore, and G. Petrova · 2022
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Europe fit for the digital age: Commission proposes new rules and actions for excellence and trust in artificial intelligence
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Near-exact recovery for tomographic inverse problems via deep learning
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Solving inverse problems with deep neural networks – robustness included?
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Training adaptive reconstruction networks for inverse problems
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The medical algorithmic audit
X. Liu, B. Glocker, M. M. McCradden, M. Ghassemi, A. K. Denniston, and L. Oakden-Rayner · 2022
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Adversarial robustness of MR image reconstruction under realistic perturbations
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Philips MR SmartSpeed: Increased speed and image quality: Driven by speed and artificial intelligence
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Implicit data crimes: Machine learning bias arising from misuse of public data
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Machine learning for medical imaging: methodological failures and recommendations for the future
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Validation and generalizability of self-supervised image reconstruction methods for undersampled MRI
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fastMRI+, clinical pathology annotations for knee and brain fully sampled magnetic resonance imaging data
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