no code implementations • 28 Aug 2023 • Riccardo Barbano, Alexander Denker, Hyungjin Chung, Tae Hoon Roh, Simon Arrdige, Peter Maass, Bangti Jin, Jong Chul Ye
Denoising diffusion models have emerged as the go-to framework for solving inverse problems in imaging.
1 code implementation • 27 Aug 2023 • Imraj RD Singh, Alexander Denker, Riccardo Barbano, Željko Kereta, Bangti Jin, Kris Thielemans, Peter Maass, Simon Arridge
Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography.
1 code implementation • 24 May 2022 • Fabian Altekrüger, Alexander Denker, Paul Hagemann, Johannes Hertrich, Peter Maass, Gabriele Steidl
Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications.
3 code implementations • 23 Nov 2021 • Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maaß, Bangti Jin
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks.
2 code implementations • 26 Oct 2021 • Alexander Denker, Maximilian Schmidt, Johannes Leuschner, Peter Maass
Over the last years, deep learning methods have become an increasingly popular choice to solve tasks from the field of inverse problems.
no code implementations • 14 Jan 2021 • Alexander Denker, Anastasia Steshina, Theresa Grooss, Frank Ueckert, Sylvia Nürnberg
We present the GeneScore, a concept of feature reduction for Machine Learning analysis of biomedical data.