Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Kereta, Zeljko"'
Regularisation is commonly used in iterative methods for solving imaging inverse problems. Many algorithms involve the evaluation of the proximal operator of the regularisation term in every iteration, leading to a significant computational overhead
Externí odkaz:
http://arxiv.org/abs/2411.00688
Autor:
Papoutsellis, Evangelos, da Costa-Luis, Casper, Deidda, Daniel, Delplancke, Claire, Duff, Margaret, Fardell, Gemma, Gillman, Ashley, Jørgensen, Jakob S., Kereta, Zeljko, Ovtchinnikov, Evgueni, Pasca, Edoardo, Schramm, Georg, Thielemans, Kris
We introduce a stochastic framework into the open--source Core Imaging Library (CIL) which enables easy development of stochastic algorithms. Five such algorithms from the literature are developed, Stochastic Gradient Descent, Stochastic Average Grad
Externí odkaz:
http://arxiv.org/abs/2406.15159
Stochastic optimisation algorithms are the de facto standard for machine learning with large amounts of data. Handling only a subset of available data in each optimisation step dramatically reduces the per-iteration computational costs, while still e
Externí odkaz:
http://arxiv.org/abs/2406.06342
Autor:
Denker, Alexander, Kereta, Zeljko, Singh, Imraj, Freudenberg, Tom, Kluth, Tobias, Maass, Peter, Arridge, Simon
Electrical impedance tomography (EIT) plays a crucial role in non-invasive imaging, with both medical and industrial applications. In this paper, we present three data-driven reconstruction methods for EIT imaging. These three approaches were origina
Externí odkaz:
http://arxiv.org/abs/2407.01559
The incorporation of generative models as regularisers within variational formulations for inverse problems has proven effective across numerous image reconstruction tasks. However, the resulting optimisation problem is often non-convex and challengi
Externí odkaz:
http://arxiv.org/abs/2404.18699
Autor:
Singh, Imraj RD, Denker, Alexander, Barbano, Riccardo, Kereta, Željko, Jin, Bangti, Thielemans, Kris, Maass, Peter, Arridge, Simon
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography. However, their application to Positron Emission Tomography (PET) is still largely un
Externí odkaz:
http://arxiv.org/abs/2308.14190
We consider a stochastic gradient descent (SGD) algorithm for solving linear inverse problems (e.g., CT image reconstruction) in the Banach space framework of variable exponent Lebesgue spaces $\ell^{(p_n)}(\mathbb{R})$. Such non-standard spaces have
Externí odkaz:
http://arxiv.org/abs/2303.09182
Autor:
Barbano, Riccardo, Antorán, Javier, Leuschner, Johannes, Hernández-Lobato, José Miguel, Jin, Bangti, Kereta, Željko
Deep learning has been widely used for solving image reconstruction tasks but its deployability has been held back due to the shortage of high-quality training data. Unsupervised learning methods, such as the deep image prior (DIP), naturally fill th
Externí odkaz:
http://arxiv.org/abs/2302.10279
Kernel ridge regression (KRR) is a popular scheme for non-linear non-parametric learning. However, existing implementations of KRR require that all the data is stored in the main memory, which severely limits the use of KRR in contexts where data siz
Externí odkaz:
http://arxiv.org/abs/2108.10411
Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities. These approaches usually require a large amount of high-quality paired training data, which is often not available in m
Externí odkaz:
http://arxiv.org/abs/2107.02572