FormTracer. A mathematica tracing package using FORM. - Anton K. Cyrol; Mario Mitter; Nils Strodthoff (Comput. Phys. Commun., 2017)

Machine Learning for Early HARQ Feedback Prediction in 5G. - Nils Strodthoff; Baris Göktepe; Thomas Schierl; Wojciech Samek; Cornelius Hellge (GLOBECOM Workshops, 2018)

Detecting and interpreting myocardial infarctions using fully convolutional neural networks. - Nils Strodthoff; Claas Strodthoff (CoRR, 2018)

Achieving Generalizable Robustness of Deep Neural Networks by Stability Training. - Jan Laermann; Wojciech Samek; Nils Strodthoff (GCPR, 2019)

Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling. - Kim Nicoli; Pan Kessel; Nils Strodthoff; Wojciech Samek; Klaus-Robert Müller; Shinichi Nakajima (CoRR, 2019)

Asymptotically Unbiased Generative Neural Sampling. - Kim A. Nicoli; Shinichi Nakajima; Nils Strodthoff; Wojciech Samek; Klaus-Robert Müller; Pan Kessel (CoRR, 2019)

Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G. - Nils Strodthoff; Baris Göktepe; Thomas Schierl; Cornelius Hellge; Wojciech Samek (IEEE J. Sel. Areas Commun., 2019)

UDSMProt: universal deep sequence models for protein classification. - Nils Strodthoff; Patrick Wagner 002; Markus Wenzel 002; Wojciech Samek (Bioinform., 2020)

USMPep: universal sequence models for major histocompatibility complex binding affinity prediction. - Johanna Vielhaben; Markus Wenzel 002; Wojciech Samek; Nils Strodthoff (BMC Bioinform., 2020)

Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning. - Stefan Bluecher; Lukas Kades; Jan M. Pawlowski; Nils Strodthoff; Julian M. Urban (CoRR, 2020)

Generative Neural Samplers for the Quantum Heisenberg Chain. - Johanna Vielhaben; Nils Strodthoff (CoRR, 2020)

PredDiff: Explanations and Interactions from Conditional Expectations. - Stefan Blücher; Nils Strodthoff (CoRR, 2021)

Predicting the Binding of SARS-CoV-2 Peptides to the Major Histocompatibility Complex with Recurrent Neural Networks. - Johanna Vielhaben; Markus Wenzel 002; Eva Weicken; Nils Strodthoff (CoRR, 2021)

On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy. - Vignesh Srinivasan; Nils Strodthoff; Jackie Ma; Alexander Binder; Klaus-Robert Müller; Wojciech Samek (CoRR, 2021)

Inferring Respiratory and Circulatory Parameters from Electrical Impedance Tomography With Deep Recurrent Models. - Nils Strodthoff; Claas Strodthoff; Tobias Becher; Norbert Weiler; Inéz Frerichs (IEEE J. Biomed. Health Informatics, 2021)

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL. - Nils Strodthoff; Patrick Wagner 002; Tobias Schaeffter; Wojciech Samek (IEEE J. Biomed. Health Informatics, 2021)

Multi-Class ECG Feature Importance Rankings: Cardiologists vs Algorithms. - Philip J. Aston; Temesgen Mehari; Alen Bosnjakovic; Peter M. Harris; Ashish Sundar; Steven E. Williams; Olaf Dössel; Axel Loewe; Claudia Nagel; Nils Strodthoff (CinC, 2022)

PredDiff: Explanations and interactions from conditional expectations. - Stefan Blücher; Johanna Vielhaben; Nils Strodthoff (Artif. Intell., 2022)

Self-supervised representation learning from 12-lead ECG data. - Temesgen Mehari; Nils Strodthoff (Comput. Biol. Medicine, 2022)

Sparse Subspace Clustering for Concept Discovery (SSCCD). - Johanna Vielhaben; Stefan Blücher; Nils Strodthoff (CoRR, 2022)

Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models. - Temesgen Mehari; Nils Strodthoff (CoRR, 2022)

Insights Into the Inner Workings of Transformer Models for Protein Function Prediction. - Markus Wenzel 002; Erik Grüner; Nils Strodthoff (CoRR, 2023)

Uncovering ECG Changes during Healthy Aging using Explainable AI. - Gabriel Ott; Yannik Schaubelt; Juan Miguel Lopez Alcaraz; Wilhelm Haverkamp; Nils Strodthoff (CoRR, 2023)

Cardiac and extracardiac discharge diagnosis prediction from emergency department ECGs using deep learning. - Nils Strodthoff; Juan Miguel Lopez Alcaraz; Wilhelm Haverkamp (CoRR, 2023)

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 002; Eva Weicken; Jackie Ma; Nils Strodthoff (Medical Image Anal., 2023)

Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models. - Juan Miguel Lopez Alcaraz; Nils Strodthoff (Trans. Mach. Learn. Res., 2023)

Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees. - Johanna Vielhaben; Stefan Bluecher; Nils Strodthoff (Trans. Mach. Learn. Res., 2023)

Diffusion-based conditional ECG generation with structured state space models. - Juan Miguel Lopez Alcaraz; Nils Strodthoff (Comput. Biol. Medicine, 2023)

Towards Quantitative Precision for ECG Analysis: Leveraging State Space Models, Self-Supervision and Patient Metadata. - Temesgen Mehari; Nils Strodthoff (IEEE J. Biomed. Health Informatics, 2023)

Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery. - Patrick Wagner 002; Temesgen Mehari; Wilhelm Haverkamp; Nils Strodthoff (Comput. Biol. Medicine, 2024)

Assessing the importance of long-range correlations for deep-learning-based sleep staging. - Tiezhi Wang; Nils Strodthoff (CoRR, 2024)

CausalConceptTS: Causal Attributions for Time Series Classification using High Fidelity Diffusion Models. - Juan Miguel Lopez Alcaraz; Nils Strodthoff (CoRR, 2024)

MDS-ED: Multimodal Decision Support in the Emergency Department - a Benchmark Dataset for Diagnoses and Deterioration Prediction in Emergency Medicine. - Juan Miguel Lopez Alcaraz; Hjalmar Bouma; Nils Strodthoff (CoRR, 2024)

CardioLab: Laboratory Values Estimation from Electrocardiogram Features - An Exploratory Study. - Juan Miguel Lopez Alcaraz; Nils Strodthoff (CoRR, 2024)

Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features. - Juan Miguel Lopez Alcaraz; Nils Strodthoff (CoRR, 2024)

CardioLab: Laboratory Values Estimation and Monitoring from Electrocardiogram Signals - A Multimodal Deep Learning Approach. - Juan Miguel Lopez Alcaraz; Nils Strodthoff (CoRR, 2024)

Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach. - Juan Miguel Lopez Alcaraz; Wilhelm Haverkamp; Nils Strodthoff (CoRR, 2024)

Beyond Scalars: Concept-Based Alignment Analysis in Vision Transformers. - Johanna Vielhaben; Dilyara Bareeva; Jim Berend; Wojciech Samek; Nils Strodthoff (CoRR, 2024)

Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study. - Juan Miguel Lopez Alcaraz; Wilhelm Haverkamp; Nils Strodthoff (CoRR, 2024)

Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks. - Stefan Bluecher; Johanna Vielhaben; Nils Strodthoff (Trans. Mach. Learn. Res., 2024)

SonicGuard Sensor - A Multichannel Acoustic Sensor for Long-Term Monitoring of Abdominal Sounds Examined through a Qualification Study. - Zahra Mansour; Verena N. Uslar; Dirk Weyhe; Danilo Hollosi; Nils Strodthoff (Sensors, 2024)

ECG Feature Importance Rankings: Cardiologists Vs. Algorithms. - Temesgen Mehari; Ashish Sundar; Alen Bosnjakovic; Peter M. Harris; Steven E. Williams; Axel Loewe; Olaf Dössel; Claudia Nagel; Nils Strodthoff; Philip J. Aston (IEEE J. Biomed. Health Informatics, 2024)

Enhancing clinical decision support with physiological waveforms - A multimodal benchmark in emergency care. - Juan Miguel Lopez Alcaraz; Hjalmar Bouma; Nils Strodthoff (Comput. Biol. Medicine, 2025)

S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models. - Tiezhi Wang; Nils Strodthoff (Comput. Biol. Medicine, 2025)

Explainable and externally validated machine learning for neuropsychiatric diagnosis via electrocardiograms. - Juan Miguel Lopez Alcaraz; Ebenezer Oloyede; David Taylor; Wilhelm Haverkamp; Nils Strodthoff (CoRR, 2025)

Benchmarking machine learning for bowel sound pattern classification from tabular features to pretrained models. - Zahra Mansour; Verena N. Uslar; Dirk Weyhe; Danilo Hollosi; Nils Strodthoff (CoRR, 2025)

Generalizable deep learning for photoplethysmography-based blood pressure estimation - A Benchmarking Study. - Mohammad Moulaeifard; Peter H. Charlton; Nils Strodthoff (CoRR, 2025)

Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches. - Mohammad Moulaeifard; Loic Coquelin; Mantas Rinkevicius; Andrius Solosenko; Oskar Pfeffer; Ciaran Bench; Nando Hegemann; Sara Vardanega; Manasi Nandi; Jordi Alastruey; Christian Heiss; Vaidotas Marozas; et al. (CoRR, 2025)

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks. - Ciaran Bench; Vivek Desai; Mohammad Moulaeifard; Nils Strodthoff; Philip J. Aston; Andrew Thompson 014 (CoRR, 2025)

FeatInv: Spatially resolved mapping from feature space to input space using conditional diffusion models. - Nils Neukirch; Johanna Vielhaben; Nils Strodthoff (CoRR, 2025)