131 citations · 315 across the 16 of their papers we have counts for
20 papers
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 +7
We propose a neural-network based survival model (SurvSurf) specifically designed for direct and simultaneous probabilistic prediction of the first hitting time of sequential event…
A study on the adequacy of common IQA measures for medical images
Anna Breger, Clemens Karner, Ian Selby +11
Image quality assessment (IQA) is standard practice in the development stage of novel machine learning algorithms that operate on images. The most commonly used IQA measures have b…
A study of why we need to reassess full reference image quality assessment with medical images
Anna Breger, Ander Biguri, Malena Sabaté Landman +11
Image quality assessment (IQA) is indispensable in clinical practice to ensure high standards, as well as in the development stage of machine learning algorithms that operate on me…
FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systems
Fan Zhang, Daniel Kreuter, Carlos Esteve-Yagüe +11
Federated learning (FL) promises to enable collaborative machine learning across healthcare sites whilst preserving data privacy. Practical deployment remains limited by statistica…
The curious case of the test set AUROC
Michael Roberts, Alon Hazan, Sören Dittmer +2
Whilst the size and complexity of ML models have rapidly and significantly increased over the past decade, the methods for assessing their performance have not kept pace. In partic…
Recent Methodological Advances in Federated Learning for Healthcare
Fan Zhang, Daniel Kreuter, Yichen Chen +10
For healthcare datasets, it is often not possible to combine data samples from multiple sites due to ethical, privacy or logistical concerns. Federated learning allows for the util…
Reinterpreting survival analysis in the universal approximator age
Sören Dittmer, Michael Roberts, Jacobus Preller +4
Survival analysis is an integral part of the statistical toolbox. However, while most domains of classical statistics have embraced deep learning, survival analysis only recently g…
Bayesian view on the training of invertible residual networks for solving linear inverse problems
Clemens Arndt, Sören Dittmer, Nick Heilenkötter +3
Learning-based methods for inverse problems, adapting to the data's inherent structure, have become ubiquitous in the last decade. Besides empirical investigations of their often r…
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…
Navigating the challenges in creating complex data systems: a development philosophy
Sören Dittmer, Michael Roberts, Julian Gilbey +6
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy a…
SELTO: Sample-Efficient Learned Topology Optimization
Sören Dittmer, David Erzmann, Henrik Harms +1
Recent developments in Deep Learning (DL) suggest a vast potential for Topology Optimization (TO). However, while there are some promising attempts, the subfield still lacks a firm…
Classification of datasets with imputed missing values: does imputation quality matter?
Tolou Shadbahr, Michael Roberts, Jan Stanczuk +15
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missi…
Unsupervised Learning of the Total Variation Flow
Tamara G. Grossmann, Sören Dittmer, Yury Korolev +1
The total variation (TV) flow generates a scale-space representation of an image based on the TV functional. This gradient flow observes desirable features for images, such as shar…
Learned convex regularizers for inverse problems
Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov +3
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.…
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 +1
Magnetic particle imaging (MPI) is an imaging modality exploiting the nonlinear magnetization behavior of (super-)paramagnetic nanoparticles to obtain a space- and often also time-…
Ground Truth Free Denoising by Optimal Transport
Sören Dittmer, Carola-Bibiane Schönlieb, Peter Maass
We present a learned unsupervised denoising method for arbitrary types of data, which we explore on images and one-dimensional signals. The training is solely based on samples of n…
A Projectional Ansatz to Reconstruction
Sören Dittmer, Peter Maass
Recently the field of inverse problems has seen a growing usage of mathematically only partially understood learned and non-learned priors. Based on first principles, we develop a…
Regularization by architecture: A deep prior approach for inverse problems
Sören Dittmer, Tobias Kluth, Peter Maass +1
The present paper studies so-called deep image prior (DIP) techniques in the context of ill-posed inverse problems. DIP networks have been recently introduced for applications in i…
Singular Values for ReLU Layers
Sören Dittmer, Emily J. King, Peter Maass
Despite their prevalence in neural networks we still lack a thorough theoretical characterization of ReLU layers. This paper aims to further our understanding of ReLU layers by stu…
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks
Jens Behrmann, Sören Dittmer, Pascal Fernsel +1
Studying the invertibility of deep neural networks (DNNs) provides a principled approach to better understand the behavior of these powerful models. Despite being a promising diagn…