Analysis of Invariance and Robustness via Invertibility of ReLU-Networks. - Jens Behrmann; Sören Dittmer; Pascal Fernsel; Peter Maaß (CoRR, 2018)

A Projectional Ansatz to Reconstruction. - Sören Dittmer; Peter Maass (CoRR, 2019)

A Deep Prior Approach to Magnetic Particle Imaging. - Sören Dittmer; Tobias Kluth; Daniel Otero Baguer; Peter Maass (MLMIR@MICCAI, 2020)

Ground Truth Free Denoising by Optimal Transport. - Sören Dittmer; Carola-Bibiane Schönlieb; Peter Maass (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)

Learned convex regularizers for inverse problems. - Subhadip Mukherjee; Sören Dittmer; Zakhar Shumaylov; Sebastian Lunz; Ozan Öktem; Carola-Bibiane Schönlieb (CoRR, 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)

Singular Values for ReLU Layers. - Sören Dittmer; Emily J. King; Peter Maass (IEEE Trans. Neural Networks Learn. Syst., 2020)

On deep learning applied to inverse problems: a chicken-and-egg problem. - Sören Dittmer (2020)

Unsupervised Learning of the Total Variation Flow. - Tamara G. Grossmann; Sören Dittmer; Yury Korolev; Carola-Bibiane Schönlieb (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 001; Evis Sala; Pietro Lió; Mishal Patel; AIX-COVNET Collaboration; et al. (CoRR, 2022)

SELTO: Sample-Efficient Learned Topology Optimization. - Sören Dittmer; David Erzmann; Henrik Harms; Peter Maass (CoRR, 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)

DL4TO : A Deep Learning Library for Sample-Efficient Topology Optimization. - David Erzmann; Sören Dittmer; Henrik Harms; Peter Maaß (GSI (1), 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)

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)

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)

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; et al. (CoRR, 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; et al. (Nat. Mac. Intell., 2023)

Data-Driven Convex Regularizers for Inverse Problems. - Subhadip Mukherjee; Sören Dittmer; Zakhar Shumaylov; Sebastian Lunz; Ozan Öktem; Carola B. Schönlieb (ICASSP, 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; Thomas R. Else; Timothy J. Sadler; Shahab Shahipasand; Arthikkaa Thavakumar; et al. (MICAD, 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)

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; et al. (CoRR, 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; et al. (CoRR, 2024)

Equivariant neural operators for gradient-consistent topology optimization. - David Erzmann; Sören Dittmer (J. Comput. Des. Eng., 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)

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)

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; et al. (Patterns, 2024)

SurvSurf: a partially monotonic neural network for first-hitting time prediction of intermittently observed discrete and continuous sequential events. - Yichen Kelly Chen; Sören Dittmer; Kinga Bernatowicz; Josep Arús-Pous; Kamen Bliznashki; John A. D. Aston; James H. F. Rudd; Carola-Bibiane Schönlieb; James Jones; Michael Roberts (CoRR, 2025)