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Peter Maaß
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2020 – today
- 2024
[i31]Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer, Peter Maaß:
Smooth Deep Saliency. CoRR abs/2404.02282 (2024)- [i30]Rudolf Herdt, Louisa Kinzel, Johann Georg Maaß, Marvin Walther, Henning Fröhlich, Tim Schubert, Peter Maass, Christian Patrick Schaaf:
Enhancing the analysis of murine neonatal ultrasonic vocalizations: Development, evaluation, and application of different mathematical models. CoRR abs/2405.12957 (2024) - [i29]Rudolf Herdt, Peter Maass:
Visualize and Paint GAN Activations. CoRR abs/2405.15636 (2024) - [i28]Alexander Denker, Zeljko Kereta, Imraj RD Singh, Tom Freudenberg, Tobias Kluth, Peter Maass, Simon R. Arridge:
Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data. CoRR abs/2407.01559 (2024) - 2023
- [c11]Rudolf Herdt
, Maximilian Schmidt, Daniel Otero Baguer, Jean Le'Clerc Arrastia, Peter Maaß:
How GAN Generators can Invert Networks in Real-Time. ACML 2023: 422-437 - [c10]David Erzmann, Sören Dittmer, Henrik Harms, Peter Maaß:
DL4TO : A Deep Learning Library for Sample-Efficient Topology Optimization. GSI (1) 2023: 543-551 - [c9]Marco Nittscher, Michael Falk Lameter, Riccardo Barbano, Johannes Leuschner, Bangti Jin, Peter Maass:
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction. MIDL 2023: 617-642 - [i27]Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer, Jean Le'Clerc Arrastia, Peter Maass:
Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time. CoRR abs/2302.02181 (2023) - [i26]Marco Nittscher, Michael Lameter, Riccardo Barbano, Johannes Leuschner, Bangti Jin, Peter Maass:
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction. CoRR abs/2303.15748 (2023) - [i25]Clemens Arndt, Alexander Denker, Sören Dittmer, Nick Heilenkötter, Meira Iske, Tobias Kluth, Peter Maass, Judith Nickel:
Invertible residual networks in the context of regularization theory for linear inverse problems. CoRR abs/2306.01335 (2023) - [i24]Derick Nganyu Tanyu, Isabel Michel, Andreas Rademacher, Jörg Kuhnert, Peter Maass:
Parameter Identification by Deep Learning of a Material Model for Granular Media. CoRR abs/2307.04166 (2023) - [i23]Imraj RD Singh, Alexander Denker, Riccardo Barbano, Zeljko Kereta, Bangti Jin, Kris Thielemans, Peter Maass, Simon R. Arridge:
Score-Based Generative Models for PET Image Reconstruction. CoRR abs/2308.14190 (2023) - [i22]Riccardo Barbano, Alexander Denker, Hyungjin Chung, Tae-Hoon Roh, Simon Arrdige, Peter Maass, Bangti Jin, Jong Chul Ye:
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Imaging Inverse Problems. CoRR abs/2308.14409 (2023) - [i21]Derick Nganyu Tanyu, Jianfeng Ning, Andreas Hauptmann, Bangti Jin, Peter Maass:
Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches. CoRR abs/2310.18636 (2023) - 2022
- [j30]José Carlos Gutiérrez Menéndez, Daniel Otero Baguer, Peter Maass:
StainCUT: Stain Normalization with Contrastive Learning. J. Imaging 8(7): 202 (2022) - [j29]Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maass, Bangti Jin:
An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction. IEEE Trans. Computational Imaging 8: 1210-1222 (2022) - [i20]Fabian Altekrüger, Alexander Denker, Paul Hagemann, Johannes Hertrich, Peter Maass, Gabriele Steidl:
PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization. CoRR abs/2205.12021 (2022) - [i19]Sören Dittmer, David Erzmann, Henrik Harms, Peter Maass:
SELTO: Sample-Efficient Learned Topology Optimization. CoRR abs/2209.05098 (2022) - [i18]Derick Nganyu Tanyu, Jianfeng Ning, Tom Freudenberg, Nick Heilenkötter, Andreas Rademacher, Uwe Iben, Peter Maass:
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems. CoRR abs/2212.03130 (2022) - 2021
- [j28]Johannes Leuschner, Maximilian Schmidt, Poulami Somanya Ganguly, Vladyslav Andriiashen, Sophia Bethany Coban, Alexander Denker, Dominik F. Bauer, Amir Hadjifaradji, Kees Joost Batenburg, Peter Maass, Maureen van Eijnatten:
Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications. J. Imaging 7(3): 44 (2021) - [j27]Jean Le'Clerc Arrastia, Nick Heilenkötter, Daniel Otero Baguer, Lena Hauberg-Lotte, Tobias Boskamp, Sonja Hetzer, Nicole Duschner, Jörg Schaller, Peter Maass:
Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma. J. Imaging 7(4): 71 (2021) - [j26]Alexander Denker, Maximilian Schmidt, Johannes Leuschner, Peter Maass:
Conditional Invertible Neural Networks for Medical Imaging. J. Imaging 7(11): 243 (2021) - [i17]Jean Le'Clerc Arrastia, Nick Heilenkötter, Daniel Otero Baguer, Lena Hauberg-Lotte, Tobias Boskamp, Sonja Hetzer, Nicole Duschner, Jörg Schaller, Peter Maaß:
Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of Basal Cell Carcinoma (BCC). CoRR abs/2103.03759 (2021) - [i16]Niklas Koenen, Marvin N. Wright, Peter Maaß, Jens Behrmann:
Generalization of the Change of Variables Formula with Applications to Residual Flows. CoRR abs/2107.04346 (2021) - [i15]Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maaß, Bangti Jin:
Is Deep Image Prior in Need of a Good Education? CoRR abs/2111.11926 (2021) - [i14]Pascal Fernsel, Peter Maass:
Regularized Orthogonal Nonnegative Matrix Factorization and K-means Clustering. CoRR abs/2112.07641 (2021) - 2020
- [j25]Sören Dittmer, Tobias Kluth, Peter Maass, Daniel Otero Baguer:
Regularization by Architecture: A Deep Prior Approach for Inverse Problems. J. Math. Imaging Vis. 62(3): 456-470 (2020) - [j24]Sören Dittmer, Emily J. King, Peter Maass:
Singular Values for ReLU Layers. IEEE Trans. Neural Networks Learn. Syst. 31(9): 3594-3605 (2020) - [c8]Sören Dittmer, Tobias Kluth, Daniel Otero Baguer, Peter Maass:
A Deep Prior Approach to Magnetic Particle Imaging. MLMIR@MICCAI 2020: 113-122 - [i13]Matthias Beckmann, Peter Maass, Judith Nickel:
Error analysis for filtered back projection reconstructions in Besov spaces. CoRR abs/2004.06618 (2020) - [i12]Tobias Kluth, Christine Bathke, Ming Jiang, Peter Maass:
Joint super-resolution image reconstruction and parameter identification in imaging operator: Analysis of bilinear operator equations, numerical solution, and application to magnetic particle imaging. CoRR abs/2004.13091 (2020) - [i11]Sören Dittmer, Carola-Bibiane Schönlieb, Peter Maass:
Ground Truth Free Denoising by Optimal Transport. CoRR abs/2007.01575 (2020) - [i10]Sören Dittmer, Tobias Kluth, Mads Thorstein Roar Henriksen, Peter Maass:
Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset. CoRR abs/2007.01593 (2020)
2010 – 2019
- 2019
- [j23]Simon R. Arridge, Peter Maass, Ozan Öktem, Carola-Bibiane Schönlieb:
Solving inverse problems using data-driven models. Acta Numer. 28: 1-174 (2019) - [j22]Johannes Leuschner, Maximilian Schmidt, Pascal Fernsel, Delf Lachmund, Tobias Boskamp, Peter Maass:
Supervised non-negative matrix factorization methods for MALDI imaging applications. Bioinform. 35(11): 1940-1947 (2019) - [c7]Christian Etmann, Sebastian Lunz, Peter Maass, Carola Schönlieb:
On the Connection Between Adversarial Robustness and Saliency Map Interpretability. ICML 2019: 1823-1832 - [c6]Di He, Ming Jiang, Alfred K. Louis, Peter Maass, Thomas Page:
Joint Bi-Modal Image Reconstruction Of Dot And Xct With An Extended Mumford-Shah Functional. ISBI 2019: 1463-1466 - [i9]Christian Etmann, Sebastian Lunz, Peter Maass, Carola-Bibiane Schönlieb:
On the Connection Between Adversarial Robustness and Saliency Map Interpretability. CoRR abs/1905.04172 (2019) - [i8]Sören Dittmer, Peter Maass:
A Projectional Ansatz to Reconstruction. CoRR abs/1907.04675 (2019) - [i7]Johannes Leuschner, Maximilian Schmidt, Daniel Otero Baguer, Peter Maaß:
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods. CoRR abs/1910.01113 (2019) - [i6]Christian Etmann, Maximilian Schmidt, Jens Behrmann, Tobias Boskamp, Lena Hauberg-Lotte, Annette Peter, Rita Casadonte, Jörg Kriegsmann, Peter Maass:
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data. CoRR abs/1912.05459 (2019) - 2018
- [j21]Jens Behrmann, Christian Etmann, Tobias Boskamp, Rita Casadonte, Jörg Kriegsmann, Peter Maaß:
Deep learning for tumor classification in imaging mass spectrometry. Bioinform. 34(7): 1215-1223 (2018) - [i5]Jens Behrmann, Sören Dittmer, Pascal Fernsel, Peter Maaß:
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks. CoRR abs/1806.09730 (2018) - [i4]Pascal Fernsel, Peter Maass:
A Survey on Surrogate Approaches to Non-negative Matrix Factorization. CoRR abs/1808.01975 (2018) - [i3]Sören Dittmer, Emily J. King, Peter Maass:
Singular Values for ReLU Layers. CoRR abs/1812.02566 (2018) - [i2]Sören Dittmer, Tobias Kluth, Peter Maass, Daniel Otero Baguer:
Regularization by architecture: A deep prior approach for inverse problems. CoRR abs/1812.03889 (2018) - 2017
- [i1]Jens Behrmann, Christian Etmann, Tobias Boskamp, Rita Casadonte, Jörg Kriegsmann, Peter Maass:
Deep Learning for Tumor Classification in Imaging Mass Spectrometry. CoRR abs/1705.01015 (2017) - 2016
- [j20]Pham Quy Muoi, Dinh Nho Hào, Peter Maass, Michael Pidcock:
Descent gradient methods for nonsmooth minimization problems in ill-posed problems. J. Comput. Appl. Math. 298: 105-122 (2016) - 2015
- [c5]Wentai Zhang, Li Shen, Thomas Page, Guojie Luo, Peng Li, Peter Maaß, Ming Jiang, Jason Cong:
FPGA Acceleration for Simultaneous Image Reconstruction and Segmentation based on the Mumford-Shah Regularization (Abstract Only). FPGA 2015: 261 - 2014
- [j19]Peter Maass, Chen Sagiv, Hans-Georg Stark, Bruno Torrésani:
Signal representation, uncertainty principles and localization measures. Adv. Comput. Math. 40(3): 597-607 (2014) - [j18]Jan Hendrik Kobarg, Peter Maass, Janina Oetjen, Oren Tropp, Eyal Hirsch, Chen Sagiv, Mohammad Golbabaee, Pierre Vandergheynst:
Numerical experiments with MALDI Imaging data. Adv. Comput. Math. 40(3): 667-682 (2014) - [c4]Peng Li, Thomas Page, Guojie Luo, Wentai Zhang, Pei Wang, Peng Zhang, Peter Maass, Ming Jiang, Jason Cong:
FPGA Acceleration for Simultaneous Medical Image Reconstruction and Segmentation. FCCM 2014: 172 - 2012
- [j17]Matthias Gehre, Tobias Kluth, Antti Lipponen, Bangti Jin, Aku Seppänen, Jari P. Kaipio, Peter Maass:
Sparsity reconstruction in electrical impedance tomography: An experimental evaluation. J. Comput. Appl. Math. 236(8): 2126-2136 (2012) - [j16]Dennis Trede, Jan Hendrik Kobarg, Janina Oetjen, Herbert Thiele, Peter Maass, Theodore Alexandrov:
On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data. J. Integr. Bioinform. 9(1) (2012) - [j15]Dennis Trede, Theodore Alexandrov, Chen Sagiv, Peter Maass:
Magnification of Label Maps With a Topology-Preserving Level-Set Method. IEEE Trans. Image Process. 21(9): 4040-4053 (2012) - [c3]Ilya Chernyavsky, Theodore Alexandrov, Peter Maass, Sergey I. Nikolenko:
A Two-Step Soft Segmentation Procedure for MALDI Imaging Mass Spectrometry Data. GCB 2012: 39-48 - [c2]Herbert Thiele, Dennis Trede, Stefan Heldmann, Theodore Alexandrov, Bernd Fischer, Peter Maass:
3D Visualisierung von Organismen. GI-Jahrestagung 2012: 1535-1544 - 2010
- [j14]Thomas Bonesky, Stephan Dahlke, Peter Maass, Thorsten Raasch:
Adaptive wavelet methods and sparsity reconstruction for inverse heat conduction problems. Adv. Comput. Math. 33(4): 385-411 (2010)
2000 – 2009
- 2009
- [j13]Theodore Alexandrov, Jens Decker, Bart J. A. Mertens, André M. Deelder, Rob A. E. M. Tollenaar, Peter Maass, Herbert Thiele:
Biomarker discovery in MALDI-TOF serum protein profiles using discrete wavelet transformation. Bioinform. 25(5): 643-649 (2009) - [j12]Kristian Bredies, Dirk A. Lorenz, Peter Maass:
A generalized conditional gradient method and its connection to an iterative shrinkage method. Comput. Optim. Appl. 42(2): 173-193 (2009) - 2008
- [j11]Hamid Reza Karimi, Peter Maass:
A convex optimisation approach to robust observer-based H∞ control design of linear parameter-varying delayed systems. Int. J. Model. Identif. Control. 4(3): 226-241 (2008) - [j10]Stephan Dahlke, Gitta Kutyniok, Peter Maass, Chen Sagiv, Hans-Georg Stark, Gerd Teschke:
The Uncertainty Principle Associated with the Continuous Shearlet Transform. Int. J. Wavelets Multiresolution Inf. Process. 6(2): 157-181 (2008) - [j9]Ludger Prünte, Peter Maass, Henning Thielemann:
Condition monitoring of linear guideways using a matched wavelet approach. Signal Process. 88(7): 1656-1670 (2008) - 2006
- [c1]Kristian Bredies, Dirk A. Lorenz, Peter Maass:
An Optimal Control Problem in Medical Image Processing. Systems, Control, Modeling and Optimization 2006: 249-259 - 2003
- [j8]Stephan Dahlke, Peter Maass, Gerd Teschke:
Reconstruction of Wideband Reflectivity Densities by Wavelet Transforms. Adv. Comput. Math. 18(2-4): 189-209 (2003) - 2001
- [j7]Peter Maaß, Sergei V. Pereverzev, Ronny Ramlau, Sergei G. Solodky:
An adaptive discretization for Tikhonov-Phillips regularization with a posteriori parameter selection. Numerische Mathematik 87(3): 485-502 (2001) - 2000
- [j6]Detlev Marpe, Gabi Blattermann, Jens Ricke, Peter Maass:
A two-layered wavelet-based algorithm for efficient lossless and lossy image compression. IEEE Trans. Circuits Syst. Video Technol. 10(7): 1094-1102 (2000) - [j5]Peter Maass, Gerd Teschke, Werner Willmann, Günter Wollmann:
Detection and classification of material attributes-a practical application of wavelet analysis. IEEE Trans. Signal Process. 48(8): 2432-2438 (2000)
1990 – 1999
- 1999
- [j4]Andreas Frommer, Peter Maass:
Fast CG-Based Methods for Tikhonov-Phillips Regularization. SIAM J. Sci. Comput. 20(5): 1831-1850 (1999) - 1997
- [b2]Alfred K. Louis, Peter Maaß, Andreas Rieder:
Wavelets - theory and applications. Pure and applied mathematics, Wiley 1997, ISBN 978-0-471-96792-7, pp. I-XVII, 1-324 - 1996
- [j3]Peter Maass, Ronny Ramlau:
Wavelet-accelerated regularization methods for hyperthermia treatment planning. Int. J. Imaging Syst. Technol. 7(3): 191-199 (1996) - 1994
- [b1]Alfred K. Louis, Peter Maaß, Andreas Rieder:
Wavelets - Theorie und Anwendungen. Teubner Studienbücher Mathematik, Teubner 1994, ISBN 978-3-519-02094-3, pp. 1-315 - 1993
- [j2]Alfred K. Louis, Peter Maass:
Contour reconstruction in 3-D X-ray CT. IEEE Trans. Medical Imaging 12(4): 764-769 (1993) - 1992
- [j1]Peter Maass, Wolfgang Treimer, U. Feye-Treimer:
Tomographic methods for 2D reconstruction with the double crystal diffractometer. IMPACT Comput. Sci. Eng. 4(3): 250-268 (1992)
Coauthor Index
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