@InProceedings{Droigk2023,
  author    = {Christine Droigk and Marco Maass and Mathias Eulers and Alfred Mertins},
  title     = {Adaption of direct Chebyshev reconstruction to an anisotropic particle model},
  journal={International Journal on Magnetic Particle Imaging},
  year = {2023},
  volume = {9},
  number = {1 Suppl 1},
 doi    = {10.18416/IJMPI.2023.2303027}
}

@InProceedings{Maass2023,
  author    = {Marco Maass and Christine Droigk and Mathias Eulers and Alfred Mertins},
  booktitle = {Proc. International Workshop on Magnetic Particle Imaging},
  title     = {A system function component model for magnetic particle imaging with anisotropic particles},
  year      = {2023},
  address   = {Aachen, Germany},
}

@InProceedings{Eulers2023,
  author    = {Mathias Eulers and Christine Droigk and Marco Maass and Alfred Mertins},
  booktitle = {Proc. International Workshop on Magnetic Particle Imaging},
  title     = {Deconvolution of direct reconstructions for MPI using Convolutional Neural Network},
  year      = {2023},
  address   = {Aachen, Germany},
}

@Article{Droigk2022b,
  author  = {Droigk, Christine and Maass, Marco and Mertins, Alfred},
  journal={International Journal on Magnetic Particle Imaging},
  title={System matrix compression using Chebyshev polynomials of first and second kind},
  year = {2022},
  doi    = {10.18416/IJMPI.2022.2212003},
  volume = {8},
  number = {2},
}

@Article{Droigk2022a,
  author  = {Droigk, Christine and Maass, Marco and Mertins, Alfred},
  journal = {Physics in Medicine & Biology},
  title   = {Direct multi-dimensional Chebyshev polynomial based reconstruction for magnetic particle imaging},
  year = {2022},
  doi    = {10.1088/1361-6560/ac4c2e},
  volume = {67},
  number = {4},
  pages = {045014},
}

@Article{Phan2022a,
   author   = {Phan, Huy and Mertins, Alfred and Baumert, Mathias},
   journal  = {IEEE Transactions on Biomedical Engineering},
   title    = {Pediatric Automatic Sleep Staging: A comparative study of 
state-of-the-art deep learning methods},
   year     = {2022},
   pages    = {1-1},
   abstract = {Despite the tremendous progress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overnight polysomnography (PSG). 
To answer the question, in this work, we conduct a large-scale comparative study on the state-of-the-art deep learning methods for pediatric automatic sleep staging. Six different deep neural networks with diverging features are adopted to evaluate a sample of more than
1,200 children across a wide spectrum of obstructive sleep apnea (OSA) severity. Our experimental results show that the individual performance of automated pediatric sleep stagers when evaluated on new subjects is equivalent to the expert-level one reported on adults. Combining the six stagers into ensemble models further boosts the staging accuracy, reaching an overall accuracy of 88.8%, a Cohens kappa of 0.852, and a macro F1-score of 85.8%. At the same time, the ensemble models lead to reduced predictive uncertainty. The results also show that the studied algorithms and their ensembles are robust to concept drift when the training and test data were recorded seven months apart and after clinical intervention. However, we show that the improvements in the staging performance are not necessarily clinically significant although the ensemble models lead to more favorable clinical measures than the six standalone models. Detailed analyses further demonstrate "almost perfect" agreement between the automatic stagers to one another and their similar patterns on the staging errors, suggesting little room for improvement.},
   doi      = {10.1109/TBME.2022.3174680},
}

@InCollection{katzberg2022b,
  author       = {Fabrice Katzberg and Alfred Mertins},
  title        = {Sparse Recovery of Sound Fields Using Measurements from Moving Microphones},
  booktitle    = {Compressed Sensing in Information Processing},
  publisher    = {Springer},
  year         = {2022},
  editor       = {Gitta Kutyniok and Holger Rauhut and Robert J. Kunsch},
  series       = {Applied and Numerical Harmonic Analysis},
  pages        = {471-505},
  isbn         = {978-3-031-09744-7},
}

@Article{Phan2022,
   author   = {Phan, Huy and Mikkelsen, Kaare and Chén, Oliver Y. and 
Koch, Philipp and Mertins, Alfred and De Vos, Maarten},
   journal  = {IEEE Transactions on Biomedical Engineering},
   title    = {SleepTransformer: Automatic Sleep Staging With 
Interpretability and Uncertainty Quantification},
   year     = {2022},
   number   = {8},
   pages    = {2456-2467},
   volume   = {69},
   abstract = {Background: Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments. Methods: Towards interpretability, this work proposes a sequence-to-sequence sleep-staging model, namely SleepTransformer. It is based on the transformer backbone and offers interpretability of the model's decisions at both the epoch and sequence level. We further propose a simple yet efficient method to quantify uncertainty in the model's decisions. The method, which is based on entropy, can serve as a metric for deferring low-confidence epochs to a human expert for further inspection. Results: Making sense of the transformer's self-attention scores for interpretability, at the epoch level, the attention scores are encoded as a heat map to highlight sleep-relevant features captured from the input EEG signal. At the sequence level, the attention scores are visualized as the influence of different neighboring epochs in an input sequence (i.e. the context) to recognition of a target epoch, mimicking the way manual scoring is done by human experts. Conclusion: Additionally, we demonstrate that SleepTransformer performs on par with existing methods on two databases of different sizes. Significance: Equipped with interpretability and the ability of uncertainty quantification, SleepTransformer holds promise for being integrated into clinical settings.},
   doi      = {10.1109/TBME.2022.3147187},
}

@InProceedings{katzberg2022,
  Title                    = {Positional Tracking of a Moving Microphone in Reverberant Scenes by Applying Perfect Sequences to Distributed Loudspeakers},
  Author                   = {Katzberg, Fabrice and Maass, Marco and Pallenberg, René} and Mertins, Alfred},
  Booktitle                = {Proc. International Workshop on Acoustic Signal Enhancement (IWAENC)},
  Year                     = {2022},

  Address                  = {Bamberg, Germany},
  Month                    = {September},
}

@InProceedings{wimmer_interspace_cvpr_2022,
  author={Wimmer, Paul and Mehnert, Jens and Condurache, Alexandru Paul},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, {CVPR} 2022, New Orleans, LA, USA, June 19-24, 2022},
  title        = {Interspace Pruning: Using Adaptive Filter Representations to Improve Training of Sparse {CNN}s},
  year       = {2022},
}

@inproceedings{Rath2,
  author    = {Matthias Rath and
               Alexandru Paul Condurache},
  editor    = {Giovanni Maria Farinella and
               Petia Radeva and
               Kadi Bouatouch},
  title     = {Improving the Sample-complexity of Deep Classification Networks with
               Invariant Integration},
  booktitle = {Proceedings of the 17th International Joint Conference on Computer
               Vision, Imaging and Computer Graphics Theory and Applications, {VISIGRAPP}
               2022, Volume 5: VISAPP, Online Streaming, February 6-8, 2022},
  pages     = {214--225},
  publisher = {{SCITEPRESS}},
  year      = {2022},
  url       = {https://doi.org/10.5220/0010872000003124},
  doi       = {10.5220/0010872000003124},
}

@article{LUST2022335,
title = {Efficient detection of adversarial, out-of-distribution and other misclassified samples},
journal = {Neurocomputing},
volume = {470},
pages = {335-343},
year = {2022},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2021.05.102},
url = {https://www.sciencedirect.com/science/article/pii/S0925231221010985},
author = {Julia Lust and Alexandru P. Condurache},
keywords = {Deep Neural Networks, Out-of-distribution detection, Adversarial example detection, Uncertainty quantification, Safety},
}

@InProceedings{Parbs2022,
   author    = {Tim J. Parbs and Philipp Koch and Alfred Mertins},
   booktitle = {Proc. European Signal Processing Conference},
   title     = {Convolutive Attention for Image Registration},
   year      = {2022},
   address   = {Belgrade, Serbia},
   month     = sep,
   pages     = {1348--1352},
   publisher = {IEEE},
   abstract  = {Elastic registration of deformed images is a vitalcomponent of many computer vision tasks, especially whenconsidering medical image data. Deep learning techniques, particularlyU-Nets, offer state-of-the-art performance, but do notyet use the rich spatial information context available in naturalimages. We propose an augmentation based on the recentlyintroduced attention mechanism to allow a U-Net to use spatialimage context. A dedicated convolutive attention scheme hasbeen developed by calculating local similarity scores of themultidimensional inputs. Additionally, a dedicated compositeerror function based on common image similarity measures isintroduced in order to further improve the registration results.To evaluate our approach, we conducted several experiments onan augmented real-world dataset containing cardiac cine MRIscans. The comparison with state-of-the-art registration schemeshighlights the potential of our approach.}, }

@article{Maass2022N,
     author = {Maass, Marco and Droigk, Christine  and Eulers, Mathias and Mertins, Alfred},
     title = {An analytical equilibrium solution to the Néel relaxation Fokker-Planck equation},
     year = {2022},
     volume = {8},
     number = {1},
     pages = {1--4},
     journal = {International Journal on Magnetic Particle Imaging},
     publisher = {Infinite Science Publishing} }

@misc{condurache21,
  author =       {Alexandru P. Condurache},
  title =        {A Safety View on Generalization for Machine Learning},
  howpublished = {International Conference on Intelligent Computer Communication and Processing (ICCP)},
  year =         {2021},
  month =        {10},
  note =         {Keynote Lecture},
  url =          {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9733460},
}

@Article{Riedel2021,
  author  = {Malte Riedel and Kawin Setsompop and Alfred Mertins and Peter Börnert},
  journal = {Magnetic Resonance in Medicine},
  title   = {Segmented simultaneous multi‐slice diffusion‐weighted imaging with navigated {3D} rigid motion correction},
  year    = {2021},
  number  = {3},
  pages   = {1701--1717},
  volume  = {86},
}

@Article{Phan2021b,
  author  = {Huy Phan and Oliver Y Chén and Minh C Tran and Philipp Koch and Alfred Mertins and Maarten De Vos},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title   = {XSleepNet: Multi-view sequential model for automatic sleep staging},
  year    = {2021},
}

@InProceedings{katzberg2021a,
  Title                    = {Spherical Harmonic Representation for Dynamic Sound-Field Measurements},
  Author                   = {Katzberg, Fabrice and Maass, Marco and Mertins, Alfred},
  Booktitle                = {Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
  Year                     = {2021},
  Address                  = {Toronto, Canada},
  Month                    =  {June},
}

@InProceedings{Koch2021,
  author    = {Koch, Philipp and Mohammad-Zadeh, Kamran and Maass, Marco and Dreier, Mark and Thomsen, Ole and Parbs, Tim J. and Phan, Huy and Mertins, Alfred},
  title     = {sEMG-Based Hand Movement Regression by Prediction of Joint Angles With Recurrent Neural Networks},
  booktitle = {43nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
  year      = {2021},
  pages     = {6519--6523},
doi = {10.1109/EMBC46164.2021.9630042},
}

@InProceedings{Phan2021a,
  author    = {Huy Phan and Huy Le Nguyen and Oliver Y Chén and Philipp Koch and Ngoc QK Duong and Ian McLoughlin and Alfred Mertins},
  booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  title     = {Self-attention generative adversarial network for speech enhancement},
  year      = {2021},
  pages     = {7103--7107},
}

@InProceedings{Pham2021b,
   author    = {Pham, Lam and Phan, Huy and Schindler, Alexander and 
King, Ross and Mertins, Alfred and McLoughlin, Ian},
   booktitle = {2021 43rd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)},
   title     = {Inception-Based Network and Multi-Spectrogram Ensemble 
Applied To Predict Respiratory Anomalies and Lung Diseases},
   year      = {2021},
   pages     = {253-256},
   doi       = {10.1109/EMBC46164.2021.9629857},
}

@Article{Pham2021a,
  author  = {L. Pham and H. Phan and T. Nguyen and R. Palaniappan and A. Mertins and I. McLoughlin},
  journal = {Digital Signal Processing},
  title   = {Robust Acoustic Scene Classification using a Multi-Spectrogram Encoder-Decoder Framework},
  year    = {2021},
  pages   = {Article ID 102943},
  volume  = {110},
}

@InProceedings{Phan2021,
  author    = {Huy Phan and Huy Le Nguyen and Oliver Y Chén and Lam Pham and Philipp Koch and Ian McLoughlin and Alfred Mertins},
  booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  title     = {Multi-view Audio and Music Classification},
  year      = {2021},
  pages     = {611--615},
}

@InProceedings{katzberg2021b,
  Title                    = {Coherence Based Trajectory Optimization for Compressive Sensing of Sound Fields},
  Author                   = {Katzberg, Fabrice and Maass, Marco and Mertins, Alfred},
  Booktitle                = {Proc. 29th European Signal Processing Conference (EUSIPCO)},
  Year                     = {2021},
  Address                  = {Dublin, Ireland},
  Month                    =  {August},
}

@Article{Pham2021,
  author  = {Lam Dang Pham and Huy Phan and Ramaswamy Palaniappan and Alfred Mertins and Ian McLoughlin},
  journal = {IEEE Journal of Biomedical and Health Informatics},
  title   = {C{NN-MoE} based framework for classification of respiratory anomalies and lung disease detection},
  year    = {2021},
}