Search Results for author: John Pauly

Found 18 papers, 7 papers with code

Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers

no code implementations17 May 2022 Arda Sahiner, Tolga Ergen, Batu Ozturkler, John Pauly, Morteza Mardani, Mert Pilanci

Vision transformers using self-attention or its proposed alternatives have demonstrated promising results in many image related tasks.

Inductive Bias

Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction

1 code implementation21 Apr 2022 Beliz Gunel, Arda Sahiner, Arjun D. Desai, Akshay S. Chaudhari, Shreyas Vasanawala, Mert Pilanci, John Pauly

Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task.

MRI Reconstruction

NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction

1 code implementation NeurIPS Workshop Deep_Invers 2021 Liyue Shen, John Pauly, Lei Xing

The method differs fundamentally from previous deep learning-based image reconstruction approaches in that NeRP exploits the internal information in an image prior, and the physics of the sparsely sampled measurements to produce a representation of the unknown subject.

Image Reconstruction Representation Learning

Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions

1 code implementation ICLR 2022 Arda Sahiner, Tolga Ergen, Batu Ozturkler, Burak Bartan, John Pauly, Morteza Mardani, Mert Pilanci

In this work, we analyze the training of Wasserstein GANs with two-layer neural network discriminators through the lens of convex duality, and for a variety of generators expose the conditions under which Wasserstein GANs can be solved exactly with convex optimization approaches, or can be represented as convex-concave games.

Image Generation

A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction

no code implementations25 May 2021 Liyue Shen, Wei Zhao, Dante Capaldi, John Pauly, Lei Xing

Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging, albeit its design and implementation have potential flaws.

Image Reconstruction

OUTCOMES: Rapid Under-sampling Optimization achieves up to 50% improvements in reconstruction accuracy for multi-contrast MRI sequences

no code implementations8 Mar 2021 Ke Wang, Enhao Gong, Yuxin Zhang, Suchadrima Banerjee, Greg Zaharchuk, John Pauly

Multi-contrast Magnetic Resonance Imaging (MRI) acquisitions from a single scan have tremendous potential to streamline exams and reduce imaging time.

Convex Regularization Behind Neural Reconstruction

no code implementations ICLR 2021 Arda Sahiner, Morteza Mardani, Batu Ozturkler, Mert Pilanci, John Pauly

Neural networks have shown tremendous potential for reconstructing high-resolution images in inverse problems.


Degrees of Freedom Analysis of Unrolled Neural Networks

no code implementations10 Jun 2019 Morteza Mardani, Qingyun Sun, Vardan Papyan, Shreyas Vasanawala, John Pauly, David Donoho

Leveraging the Stein's Unbiased Risk Estimator (SURE), this paper analyzes the generalization risk with its bias and variance components for recurrent unrolled networks.

Image Restoration

Neural Proximal Gradient Descent for Compressive Imaging

1 code implementation NeurIPS 2018 Morteza Mardani, Qingyun Sun, Shreyas Vasawanala, Vardan Papyan, Hatef Monajemi, John Pauly, David Donoho

Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information.

Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet

1 code implementation15 Mar 2018 Jaeyeon Yoon, Enhao Gong, Itthi Chatnuntawech, Berkin Bilgic, Jingu Lee, Woojin Jung, Jingyu Ko, Hosan Jung, Kawin Setsompop, Greg Zaharchuk, Eung Yeop Kim, John Pauly, Jong-Ho Lee

The QSMnet maps of the test dataset were compared with those from TKD and MEDI for image quality and consistency in multiple head orientations.

Image and Video Processing

200x Low-dose PET Reconstruction using Deep Learning

no code implementations12 Dec 2017 Junshen Xu, Enhao Gong, John Pauly, Greg Zaharchuk

Experiments shows the proposed method can reconstruct low-dose PET image to a standard-dose quality with only two-hundredth dose.

Decoder Image Reconstruction

Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery

no code implementations27 Nov 2017 Morteza Mardani, Hatef Monajemi, Vardan Papyan, Shreyas Vasanawala, David Donoho, John Pauly

Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually "plausible" and physically "feasible" images with minimal hallucination.

Denoising Hallucination +1

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