Sören Dittmer

According to our database, Sören Dittmer authored at least 27 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Recent methodological advances in federated learning for healthcare.

Fan Zhang, Daniel Kreuter, Yichen Chen, Sören Dittmer, Samuel Tull, Tolou Shadbahr, Martijn Schut, Folkert W. Asselbergs, Sujoy Kar, Suthesh Sivapalaratnam, Sophie Williams, Mickey Koh, Yvonne Henskens, Bart de Wit, Umberto D'alessandro, Bubacarr Bah, Ousman Secka, Parashkev Nachev, Rajeev Gupta, Sara Trompeter, Nancy Boeckx, Christine van Laer, Gordon A. Awandare, Kwabena Sarpong, Lucas Amenga-Etego, Mathie Leers, Mirelle Huijskens, Samuel McDermott, Willem H. Ouwehand, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb, Nicholas S. Gleadall, Michael Roberts

Patterns, 2024

Publisher Correction: The curious case of the test set AUROC.

Michael Roberts, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb

Nat. Mac. Intell., 2024

The curious case of the test set AUROC.

Michael Roberts, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb

Nat. Mac. Intell., 2024

Equivariant neural operators for gradient-consistent topology optimization.

David Erzmann, Sören Dittmer

J. Comput. Des. Eng., 2024

A study on the adequacy of common IQA measures for medical images.

Anna Breger, Clemens Karner, Ian Selby, Janek Gröhl, Sören Dittmer, Edward Lilley, Judith Babar, Jake Beckford, Timothy J. Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, Michael Roberts, Carola-Bibiane Schönlieb

CoRR, 2024

A study of why we need to reassess full reference image quality assessment with medical images.

Anna Breger, Ander Biguri, Malena Sabaté Landman, Ian Selby, Nicole Amberg, Elisabeth Brunner, Janek Gröhl, Sepideh Hatamikia, Clemens Karner, Lipeng Ning, Sören Dittmer, Michael Roberts, AIX-COVNET Collaboration, Carola-Bibiane Schönlieb

CoRR, 2024

FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization.

Fan Zhang, Carlos Esteve-Yagüe, Sören Dittmer, Carola-Bibiane Schönlieb, Michael Roberts

CoRR, 2024

Data-Driven Convex Regularizers for Inverse Problems.

Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov, Sebastian Lunz, Ozan Öktem, Carola B. Schönlieb

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

Navigating the development challenges in creating complex data systems.

Sören Dittmer, Michael Roberts, Julian D. Gilbey, Ander Biguri, Ian Selby, Anna Breger, Matthew Thorpe, Jonathan R. Weir-McCall, Effrossyni Gkrania-Klotsas, Anna Korhonen, Emily R. Jefferson, Georg Langs, Guang Yang, Helmut Prosch, Jan Stanczuk, Jing Tang, Judith Babar, Lorena Escudero Sanchez, Philip Teare, Mishal Patel, Marcel Wassin, Markus Holzer, Nicholas Walton, Pietro Lió, Tolou Shadbahr, Evis Sala, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb

Nat. Mac. Intell., July, 2023

Recent Methodological Advances in Federated Learning for Healthcare.

Fan Zhang, Daniel Kreuter, Yichen Chen, Sören Dittmer, Samuel Tull, Tolou Shadbahr, BloodCounts Collaboration, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb, Nicholas S. Gleadall, Michael Roberts

CoRR, 2023

Reinterpreting survival analysis in the universal approximator age.

Sören Dittmer, Michael Roberts, Jacobus Preller, AIX-COVNET Collaboration, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb

CoRR, 2023

Bayesian view on the training of invertible residual networks for solving linear inverse problems.

Clemens Arndt, Sören Dittmer, Nick Heilenkötter, Meira Iske, Tobias Kluth, Judith Nickel

CoRR, 2023

Invertible residual networks in the context of regularization theory for linear inverse problems.

Clemens Arndt, Alexander Denker, Sören Dittmer, Nick Heilenkötter, Meira Iske, Tobias Kluth, Peter Maass, Judith Nickel

CoRR, 2023

DL4TO : A Deep Learning Library for Sample-Efficient Topology Optimization.

David Erzmann, Sören Dittmer, Henrik Harms, Peter Maaß

Proceedings of the Geometric Science of Information - 6th International Conference, 2023

2022

Navigating the challenges in creating complex data systems: a development philosophy.

Sören Dittmer, Michael Roberts, Julian D. Gilbey, Ander Biguri, AIX-COVNET Collaboration, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb

CoRR, 2022

SELTO: Sample-Efficient Learned Topology Optimization.

Sören Dittmer, David Erzmann, Henrik Harms, Peter Maass

CoRR, 2022

Classification of datasets with imputed missing values: does imputation quality matter?

Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian D. Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramón Viñas Torné, Evis Sala, Pietro Lió, Mishal Patel, AIX-COVNET Collaboration, James H. F. Rudd, Tuomas Mirtti, Antti Rannikko, John A. D. Aston, Jing Tang, Carola-Bibiane Schönlieb

CoRR, 2022

Unsupervised Learning of the Total Variation Flow.

Tamara G. Grossmann, Sören Dittmer, Yury Korolev, Carola-Bibiane Schönlieb

CoRR, 2022

2020

On deep learning applied to inverse problems: a chicken-and-egg problem.

Sören Dittmer

PhD thesis, 2020

Singular Values for ReLU Layers.

Sören Dittmer, Emily J. King, Peter Maass

IEEE Trans. Neural Networks Learn. Syst., 2020

Regularization by Architecture: A Deep Prior Approach for Inverse Problems.

Sören Dittmer, Tobias Kluth, Peter Maass, Daniel Otero Baguer

J. Math. Imaging Vis., 2020

Learned convex regularizers for inverse problems.

Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov, Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb

CoRR, 2020

Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset.

Sören Dittmer, Tobias Kluth, Mads Thorstein Roar Henriksen, Peter Maass

CoRR, 2020

Ground Truth Free Denoising by Optimal Transport.

Sören Dittmer, Carola-Bibiane Schönlieb, Peter Maass

CoRR, 2020

A Deep Prior Approach to Magnetic Particle Imaging.

Sören Dittmer, Tobias Kluth, Daniel Otero Baguer, Peter Maass

Proceedings of the Machine Learning for Medical Image Reconstruction, 2020

2019

A Projectional Ansatz to Reconstruction.

Sören Dittmer, Peter Maass

CoRR, 2019

2018

Analysis of Invariance and Robustness via Invertibility of ReLU-Networks.

Jens Behrmann, Sören Dittmer, Pascal Fernsel, Peter Maaß

CoRR, 2018