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Nils Strodthoff
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2020 – today
- 2024
- [j17]Patrick Wagner, Temesgen Mehari
, Wilhelm Haverkamp
, Nils Strodthoff:
Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery. Comput. Biol. Medicine 176: 108525 (2024) - [j16]Zahra Mansour, Verena N. Uslar
, Dirk Weyhe, Danilo Hollosi, Nils Strodthoff
:
SonicGuard Sensor - A Multichannel Acoustic Sensor for Long-Term Monitoring of Abdominal Sounds Examined through a Qualification Study. Sensors 24(6): 1843 (2024) - [j15]Temesgen Mehari
, Ashish Sundar, Alen Bosnjakovic
, Peter M. Harris, Steven E. Williams
, Axel Loewe
, Olaf Dössel
, Claudia Nagel
, Nils Strodthoff
, Philip J. Aston
:
ECG Feature Importance Rankings: Cardiologists Vs. Algorithms. IEEE J. Biomed. Health Informatics 28(4): 2014-2024 (2024) - [j14]Stefan Bluecher, Johanna Vielhaben, Nils Strodthoff:
Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks. Trans. Mach. Learn. Res. 2024 (2024) - [i31]Stefan Blücher, Johanna Vielhaben, Nils Strodthoff:
Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks. CoRR abs/2401.06654 (2024) - [i30]Tiezhi Wang, Nils Strodthoff:
Assessing the importance of long-range correlations for deep-learning-based sleep staging. CoRR abs/2402.17779 (2024) - [i29]Juan Miguel Lopez Alcaraz, Nils Strodthoff:
CausalConceptTS: Causal Attributions for Time Series Classification using High Fidelity Diffusion Models. CoRR abs/2405.15871 (2024) - [i28]Juan Miguel Lopez Alcaraz, Hjalmar Bouma, Nils Strodthoff:
MDS-ED: Multimodal Decision Support in the Emergency Department - a Benchmark Dataset for Diagnoses and Deterioration Prediction in Emergency Medicine. CoRR abs/2407.17856 (2024) - [i27]Juan Miguel Lopez Alcaraz, Nils Strodthoff:
CardioLab: Laboratory Values Estimation from Electrocardiogram Features - An Exploratory Study. CoRR abs/2407.18629 (2024) - 2023
- [j13]Juan Miguel Lopez Alcaraz
, Nils Strodthoff:
Diffusion-based conditional ECG generation with structured state space models. Comput. Biol. Medicine 163: 107115 (2023) - [j12]Maximilian Springenberg
, Annika Frommholz, Markus Wenzel
, Eva Weicken, Jackie Ma
, Nils Strodthoff
:
From modern CNNs to vision transformers: Assessing the performance, robustness, and classification strategies of deep learning models in histopathology. Medical Image Anal. 87: 102809 (2023) - [j11]Temesgen Mehari
, Nils Strodthoff
:
Towards Quantitative Precision for ECG Analysis: Leveraging State Space Models, Self-Supervision and Patient Metadata. IEEE J. Biomed. Health Informatics 27(11): 5326-5334 (2023) - [j10]Juan Miguel Lopez Alcaraz, Nils Strodthoff:
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models. Trans. Mach. Learn. Res. 2023 (2023) - [j9]Johanna Vielhaben, Stefan Bluecher, Nils Strodthoff:
Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees. Trans. Mach. Learn. Res. 2023 (2023) - [i26]Juan Miguel Lopez Alcaraz, Nils Strodthoff:
Diffusion-based Conditional ECG Generation with Structured State Space Models. CoRR abs/2301.08227 (2023) - [i25]Johanna Vielhaben, Stefan Blücher, Nils Strodthoff:
Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees. CoRR abs/2301.11911 (2023) - [i24]Temesgen Mehari, Ashish Sundar, Alen Bosnjakovic, Peter M. Harris, Steven E. Williams, Axel Loewe, Olaf Dössel, Claudia Nagel, Nils Strodthoff, Philip J. Aston:
ECG Feature Importance Rankings: Cardiologists vs. Algorithms. CoRR abs/2304.02577 (2023) - [i23]Patrick Wagner, Temesgen Mehari, Wilhelm Haverkamp, Nils Strodthoff:
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery. CoRR abs/2305.17043 (2023) - [i22]Temesgen Mehari, Nils Strodthoff:
Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata. CoRR abs/2308.15291 (2023) - [i21]Markus Wenzel, Erik Grüner, Nils Strodthoff:
Insights Into the Inner Workings of Transformer Models for Protein Function Prediction. CoRR abs/2309.03631 (2023) - [i20]Tiezhi Wang, Nils Strodthoff:
S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models. CoRR abs/2310.06715 (2023) - [i19]Gabriel Ott, Yannik Schaubelt, Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp, Nils Strodthoff:
Uncovering ECG Changes during Healthy Aging using Explainable AI. CoRR abs/2310.07463 (2023) - [i18]Nils Strodthoff, Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp:
Cardiac and extracardiac discharge diagnosis prediction from emergency department ECGs using deep learning. CoRR abs/2312.11050 (2023) - 2022
- [j8]Stefan Blücher
, Johanna Vielhaben
, Nils Strodthoff
:
PredDiff: Explanations and interactions from conditional expectations. Artif. Intell. 312: 103774 (2022) - [j7]Temesgen Mehari, Nils Strodthoff:
Self-supervised representation learning from 12-lead ECG data. Comput. Biol. Medicine 141: 105114 (2022) - [c3]Philip J. Aston, Temesgen Mehari, Alen Bosnjakovic, Peter M. Harris, Ashish Sundar, Steven E. Williams, Olaf Dössel, Axel Loewe
, Claudia Nagel, Nils Strodthoff:
Multi-Class ECG Feature Importance Rankings: Cardiologists vs Algorithms. CinC 2022: 1-4 - [i17]Johanna Vielhaben, Stefan Blücher, Nils Strodthoff:
Sparse Subspace Clustering for Concept Discovery (SSCCD). CoRR abs/2203.06043 (2022) - [i16]Maximilian Springenberg, Annika Frommholz, Markus Wenzel, Eva Weicken, Jackie Ma, Nils Strodthoff:
From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology. CoRR abs/2204.05044 (2022) - [i15]Juan Miguel Lopez Alcaraz, Nils Strodthoff:
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models. CoRR abs/2208.09399 (2022) - [i14]Temesgen Mehari, Nils Strodthoff:
Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models. CoRR abs/2211.07579 (2022) - 2021
- [j6]Nils Strodthoff
, Patrick Wagner, Tobias Schaeffter
, Wojciech Samek
:
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL. IEEE J. Biomed. Health Informatics 25(5): 1519-1528 (2021) - [j5]Nils Strodthoff
, Claas Strodthoff
, Tobias Becher, Norbert Weiler, Inéz Frerichs
:
Inferring Respiratory and Circulatory Parameters from Electrical Impedance Tomography With Deep Recurrent Models. IEEE J. Biomed. Health Informatics 25(8): 3105-3111 (2021) - [i13]Stefan Blücher, Nils Strodthoff:
PredDiff: Explanations and Interactions from Conditional Expectations. CoRR abs/2102.13519 (2021) - [i12]Temesgen Mehari, Nils Strodthoff:
Self-supervised representation learning from 12-lead ECG data. CoRR abs/2103.12676 (2021) - [i11]Johanna Vielhaben, Markus Wenzel, Eva Weicken, Nils Strodthoff:
Predicting the Binding of SARS-CoV-2 Peptides to the Major Histocompatibility Complex with Recurrent Neural Networks. CoRR abs/2104.08237 (2021) - [i10]Vignesh Srinivasan, Nils Strodthoff, Jackie Ma, Alexander Binder, Klaus-Robert Müller, Wojciech Samek:
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy. CoRR abs/2106.13497 (2021) - 2020
- [j4]Nils Strodthoff
, Patrick Wagner, Markus Wenzel, Wojciech Samek:
UDSMProt: universal deep sequence models for protein classification. Bioinform. 36(8): 2401-2409 (2020) - [j3]Johanna Vielhaben
, Markus Wenzel, Wojciech Samek, Nils Strodthoff
:
USMPep: universal sequence models for major histocompatibility complex binding affinity prediction. BMC Bioinform. 21(1): 279 (2020) - [i9]Stefan Bluecher
, Lukas Kades, Jan M. Pawlowski, Nils Strodthoff, Julian M. Urban:
Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning. CoRR abs/2003.01504 (2020) - [i8]Nils Strodthoff, Patrick Wagner, Tobias Schaeffter, Wojciech Samek:
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL. CoRR abs/2004.13701 (2020) - [i7]Nils Strodthoff, Claas Strodthoff, Tobias Becher, Norbert Weiler, Inéz Frerichs:
Inferring respiratory and circulatory parameters from electrical impedance tomography with deep recurrent models. CoRR abs/2010.09622 (2020) - [i6]Johanna Vielhaben, Nils Strodthoff:
Generative Neural Samplers for the Quantum Heisenberg Chain. CoRR abs/2012.10264 (2020)
2010 – 2019
- 2019
- [j2]Nils Strodthoff
, Baris Göktepe
, Thomas Schierl, Cornelius Hellge
, Wojciech Samek
:
Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G. IEEE J. Sel. Areas Commun. 37(11): 2573-2587 (2019) - [c2]Jan Laermann, Wojciech Samek, Nils Strodthoff:
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training. GCPR 2019: 360-373 - [i5]Kim Nicoli
, Pan Kessel, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, Shinichi Nakajima:
Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling. CoRR abs/1903.11048 (2019) - [i4]Jan Laermann, Wojciech Samek, Nils Strodthoff:
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training. CoRR abs/1906.00735 (2019) - [i3]Kim A. Nicoli
, Shinichi Nakajima, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, Pan Kessel:
Asymptotically Unbiased Generative Neural Sampling. CoRR abs/1910.13496 (2019) - 2018
- [c1]Nils Strodthoff, Baris Göktepe
, Thomas Schierl, Wojciech Samek, Cornelius Hellge:
Machine Learning for Early HARQ Feedback Prediction in 5G. GLOBECOM Workshops 2018: 1-6 - [i2]Nils Strodthoff, Claas Strodthoff:
Detecting and interpreting myocardial infarctions using fully convolutional neural networks. CoRR abs/1806.07385 (2018) - [i1]Nils Strodthoff, Baris Göktepe, Thomas Schierl, Cornelius Hellge, Wojciech Samek:
Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G. CoRR abs/1807.10495 (2018) - 2017
- [j1]Anton K. Cyrol
, Mario Mitter, Nils Strodthoff
:
FormTracer. A mathematica tracing package using FORM. Comput. Phys. Commun. 219: 346-352 (2017)
Coauthor Index
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last updated on 2024-08-25 19:10 CEST by the dblp team
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