Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Denker, Alexander"'
DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised $h$-transform
Autor:
Denker, Alexander, Vargas, Francisco, Padhy, Shreyas, Didi, Kieran, Mathis, Simon, Dutordoir, Vincent, Barbano, Riccardo, Mathieu, Emile, Komorowska, Urszula Julia, Lio, Pietro
Generative modelling paradigms based on denoising diffusion processes have emerged as a leading candidate for conditional sampling in inverse problems. In many real-world applications, we often have access to large, expensively trained unconditional
Externí odkaz:
http://arxiv.org/abs/2406.01781
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:
Barbano, Riccardo, Denker, Alexander, Chung, Hyungjin, Roh, Tae Hoon, Arridge, Simon, Maass, Peter, Jin, Bangti, Ye, Jong Chul
Denoising diffusion models have emerged as the go-to generative framework for solving inverse problems in imaging. A critical concern regarding these models is their performance on out-of-distribution tasks, which remains an under-explored challenge.
Externí odkaz:
http://arxiv.org/abs/2308.14409
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
Autor:
Arndt, Clemens, Denker, Alexander, Dittmer, Sören, Heilenkötter, Nick, Iske, Meira, Kluth, Tobias, Maass, Peter, Nickel, Judith
Publikováno v:
Inverse Problems 39 125018 (2023)
Learned inverse problem solvers exhibit remarkable performance in applications like image reconstruction tasks. These data-driven reconstruction methods often follow a two-step scheme. First, one trains the often neural network-based reconstruction s
Externí odkaz:
http://arxiv.org/abs/2306.01335
Autor:
Altekrüger, Fabian, Denker, Alexander, Hagemann, Paul, Hertrich, Johannes, Maass, Peter, Steidl, Gabriele
Publikováno v:
Inverse Problems, Volume 39, Number 6, 2023
Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications. In this paper, we introduce a powerful regularizer for the variational modeling of inverse problems in im
Externí odkaz:
http://arxiv.org/abs/2205.12021
Autor:
Barbano, Riccardo, Leuschner, Johannes, Schmidt, Maximilian, Denker, Alexander, Hauptmann, Andreas, Maaß, Peter, Jin, Bangti
Publikováno v:
in IEEE Transactions on Computational Imaging, vol. 8, pp. 1210-1222, 2022
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks. DIP represents the image to be recovered as the output of a deep convolutional neural network, and learns the network's parameters such
Externí odkaz:
http://arxiv.org/abs/2111.11926
Over the last years, deep learning methods have become an increasingly popular choice to solve tasks from the field of inverse problems. Many of these new data-driven methods have produced impressive results, although most only give point estimates f
Externí odkaz:
http://arxiv.org/abs/2110.14520
We present the GeneScore, a concept of feature reduction for Machine Learning analysis of biomedical data. Using expert knowledge, the GeneScore integrates different molecular data types into a single score. We show that the GeneScore is superior to
Externí odkaz:
http://arxiv.org/abs/2101.05546