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Found 8 papers, 2 papers with code
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems
In this work, a new empirical sampling method is proposed that computes multiple solutions of a tomographic inverse problem that are consistent with the same acquired measurement data.
Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks
AmbientGANs established using the proposed training procedure are systematically validated in a controlled way using computer-simulated magnetic resonance imaging (MRI) data corresponding to a stylized imaging system.
Advancing the AmbientGAN for learning stochastic object models
Medical imaging systems are commonly assessed and optimized by use of objective-measures of image quality (IQ) that quantify the performance of an observer at specific tasks.
On hallucinations in tomographic image reconstruction
The behavior of different reconstruction methods under the proposed formalism is discussed with the help of the numerical studies.
Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction
To circumvent this problem, in this work, a framework for reconstructing images from incomplete measurements is proposed that is formulated in the latent space of invertible neural network-based generative models.
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs
To circumvent this, in this work, a new Progressive Growing AmbientGAN (ProAmGAN) strategy is developed for establishing SOMs from medical imaging measurements.
Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks
Medical image reconstruction is typically an ill-posed inverse problem.
Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements
However, because medical imaging systems record imaging measurements that are noisy and indirect representations of object properties, GANs cannot be directly applied to establish stochastic models of objects to-be-imaged.
