26 citations · 79 across the 11 of their papers we have counts for
12 papers
Supervised Guidance Training for Infinite-Dimensional Diffusion Models
Elizabeth L. Baker, Alexander Denker, Jes Frellsen
Score-based diffusion models have recently been extended to infinite-dimensional function spaces, with uses such as inverse problems arising from partial differential equations. In…
Deep Learning Based Reconstruction Methods for Electrical Impedance Tomography
Alexander Denker, Fabio Margotti, Jianfeng Ning +5
Electrical Impedance Tomography (EIT) is a powerful imaging modality widely used in medical diagnostics, industrial monitoring, and environmental studies. The EIT inverse problem i…
Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data
Alexander Denker, Zeljko Kereta, Imraj Singh +4
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 recon…
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised -transform
Alexander Denker, Francisco Vargas, Shreyas Padhy +7
Generative modelling paradigms based on denoising diffusion processes have emerged as a leading candidate for conditional sampling in inverse problems. In many real-world applicati…
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction
Riccardo Barbano, Alexander Denker, Hyungjin Chung +5
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…
Score-Based Generative Models for PET Image Reconstruction
Imraj RD Singh, Alexander Denker, Riccardo Barbano +5
Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography. However, their…
Invertible residual networks in the context of regularization theory for linear inverse problems
Clemens Arndt, Alexander Denker, Sören Dittmer +5
Learned inverse problem solvers exhibit remarkable performance in applications like image reconstruction tasks. These data-driven reconstruction methods often follow a two-step sch…
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization
Fabian Altekrüger, Alexander Denker, Paul Hagemann +3
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 power…
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt +4
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 c…
Conditional Invertible Neural Networks for Medical Imaging
Alexander Denker, Maximilian Schmidt, Johannes Leuschner +1
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…
Feature reduction for machine learning on molecular features: The GeneScore
Alexander Denker, Anastasia Steshina, Theresa Grooss +2
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…
Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction
Alexander Denker, Maximilian Schmidt, Johannes Leuschner +2
Image reconstruction from computed tomography (CT) measurement is a challenging statistical inverse problem since a high-dimensional conditional distribution needs to be estimated.…