176 citations · 467 across the 10 of their papers we have counts for
11 papers
Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata
Temesgen Mehari, Nils Strodthoff
Deep learning has emerged as the preferred modeling approach for automatic ECG analysis. In this study, we investigate three elements aimed at improving the quantitative accuracy o…
ECG Feature Importance Rankings: Cardiologists vs. Algorithms
Temesgen Mehari, Ashish Sundar, Alen Bosnjakovic +7
Feature importance methods promise to provide a ranking of features according to importance for a given classification task. A wide range of methods exist but their rankings often…
From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology
Maximilian Springenberg, Annika Frommholz, Markus Wenzel +3
While machine learning is currently transforming the field of histopathology, the domain lacks a comprehensive evaluation of state-of-the-art models based on essential but compleme…
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy
Vignesh Srinivasan, Nils Strodthoff, Jackie Ma +3
There is an increasing number of medical use-cases where classification algorithms based on deep neural networks reach performance levels that are competitive with human medical ex…
Inferring respiratory and circulatory parameters from electrical impedance tomography with deep recurrent models
Nils Strodthoff, Claas Strodthoff, Tobias Becher +2
Electrical impedance tomography (EIT) is a noninvasive imaging modality that allows a continuous assessment of changes in regional bioimpedance of different organs. One of its most…
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
Nils Strodthoff, Patrick Wagner, Tobias Schaeffter +1
Electrocardiography is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by automatic interpretation algorithms. The progress in the…
Asymptotically unbiased estimation of physical observables with neural samplers
Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff +3
We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling p…
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
Jan Laermann, Wojciech Samek, Nils Strodthoff
We study the recently introduced stability training as a general-purpose method to increase the robustness of deep neural networks against input perturbations. In particular, we ex…
Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling
Kim Nicoli, Pan Kessel, Nils Strodthoff +3
In this comment on "Solving Statistical Mechanics Using Variational Autoregressive Networks" by Wu et al., we propose a subtle yet powerful modification of their approach. We show…
Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G
Nils Strodthoff, Barış Göktepe, Thomas Schierl +2
We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine learning techniques as a path towards ultra-reliable and low-latency communicatio…
Detecting and interpreting myocardial infarction using fully convolutional neural networks
Nils Strodthoff, Claas Strodthoff
Objective: We aim to provide an algorithm for the detection of myocardial infarction that operates directly on ECG data without any preprocessing and to investigate its decision cr…