% generated by bibtexbrowser % % Encoding: UTF-8 @inproceedings{Sprenger-et-al-ismrm14, author = {T. Sprenger and J.I. Sperl and B. Fernandez and V. Golkov and E.T. Tan and C.J. Hardy and L. Marinelli and M. Czisch and P. Sämann and A. Haase and M.I. Menzel}, title = {Novel Acquisition Scheme for Diffusion Kurtosis Imaging Based on Compressed-Sensing Accelerated {DSI} Yielding Superior Image Quality}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2014}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, } @inproceedings{Sperl-et-al-ismrm14, author = {J.I. Sperl and T. Sprenger and E.T. Tan and V. Golkov and M.I. Menzel and C.J. Hardy and L. Marinelli}, title = {Total Variation-Regularized Compressed Sensing Reconstruction for Multi-Shell Diffusion Kurtosis Imaging}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2014}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing, total variation}, } @inproceedings{Golkov-et-al-ismrm14-6d-cs, author = {V. Golkov and M.I. Menzel and T. Sprenger and M. Souiai and A. Haase and D. Cremers and J.I. Sperl}, title = {Direct Reconstruction of the Average Diffusion Propagator with Simultaneous Compressed-Sensing-Accelerated Diffusion Spectrum Imaging and Image Denoising by Means of Total Generalized Variation Regularization}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2014}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing, total generalized variation, primal-dual}, } @inproceedings{Golkov-et-al-ismrm14-semi-joint, author = {V. Golkov and M.I. Menzel and T. Sprenger and A. Haase and D. Cremers and J.I. Sperl}, title = {Semi-Joint Reconstruction for Diffusion {MRI} Denoising Imposing Similarity of Edges in Similar Diffusion-Weighted Images}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2014}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, } @inproceedings{Golkov-et-al-ohbm14, author = {V. Golkov and M.I. Menzel and T. Sprenger and M. Souiai and A. Haase and D. Cremers and J.I. Sperl}, title = {Improved Diffusion Kurtosis Imaging and Direct Propagator Estimation Using {6-D} Compressed Sensing}, booktitle = {Organization for Human Brain Mapping (OHBM) Annual Meeting}, year = {2014}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing, total generalized variation, primal-dual}, } @incollection{Golkov-et-al-cdmri14, author = {V. Golkov and J.I. Sperl and M.I. Menzel and T. Sprenger and E.T. Tan and L. Marinelli and C.J. Hardy and A. Haase and D. Cremers}, title = {Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per {q}-Space Coordinate}, booktitle = {Computational Diffusion {MRI}}, publisher = {Springer}, year = {2014}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, total generalized variation, super-resolution, primal-dual}, award = {Book Chapter, and Oral Presentation at {MICCAI} 2014 Workshop on Computational Diffusion {MRI}}, } @inproceedings{Golkov-et-al-esmrmb13-comparison, author = {V. Golkov and T. Sprenger and A. Menini and M.I. Menzel and D. Cremers and J.I. Sperl}, title = {Effects of Low-Rank Constraints, Line-Process Denoising, and {q}-Space Compressed Sensing on Diffusion {MR} Image Reconstruction and Kurtosis Tensor Estimation}, booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, year = {2013}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, award = {Oral Presentation}, } @inproceedings{Golkov-et-al-esmrmb13-iic-nnc, author = {V. Golkov and T. Sprenger and M.I. Menzel and D. Cremers and J.I. Sperl}, title = {Line-Process-Based Joint {SENSE} Reconstruction of Diffusion Images with Intensity Inhomogeneity Correction and Noise Non-Stationarity Correction}, booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, year = {2013}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging}, award = {Certificate of Merit Award}, } @inproceedings{Golkov-et-al-dsismrm13, author = {V. Golkov and M.I. Menzel and T. Sprenger and A. Menini and D. Cremers and J.I. Sperl}, title = {Reconstruction, Regularization, and Quality in Diffusion {MRI} Using the Example of Accelerated Diffusion Spectrum Imaging}, booktitle = {16th Annual Meeting of the German Chapter of the {ISMRM}}, year = {2013}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, award = {Oral Presentation}, } @inproceedings{Golkov-et-al-podstrana13, author = {V. Golkov and M.I. Menzel and T. Sprenger and A. Menini and D. Cremers and J.I. Sperl}, title = {Corrected Joint {SENSE} Reconstruction, Low-Rank Constraints, and Compressed-Sensing-Accelerated Diffusion Spectrum Imaging in Denoising and Kurtosis Tensor Estimation}, booktitle = {{ISMRM} Workshop on Diffusion as a Probe of Neural Tissue Microstructure}, year = {2013}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, } @inproceedings{Sprenger-et-al-ismrm13, author = {T. Sprenger and B. Fernandez and J.I. Sperl and V. Golkov and M. Bach and E.T. Tan and K.F. King and C.J. Hardy and L. Marinelli and M. Czisch and P. Sämann and A. Haase and M.I. Menzel}, title = {{SNR}-dependent Quality Assessment of Compressed-Sensing-Accelerated Diffusion Spectrum Imaging Using a Fiber Crossing Phantom}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2013}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, } @inproceedings{Sperl-et-al-ismrm13, author = {J.I. Sperl and E.T. Tan and T. Sprenger and V. Golkov and K.F. King and C.J. Hardy and L. Marinelli and M.I. Menzel}, title = {Phase Sensitive Reconstruction in Diffusion Spectrum Imaging Enabling Velocity Encoding and Unbiased Noise Distribution}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2013}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging}, } @inproceedings{Golkov-et-al-ismrm13, author = {V. Golkov and T. Sprenger and M.I. Menzel and E.T. Tan and K.F. King and C.J. Hardy and L. Marinelli and D. Cremers and J.I. Sperl}, title = {Noise Reduction in Accelerated Diffusion Spectrum Imaging through Integration of {SENSE} Reconstruction into Joint Reconstruction in Combination with {q}-Space Compressed Sensing}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2013}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, } @inproceedings{Sprenger-et-al-esmrmb12, author = {T. Sprenger and B. Fernandez and M. Bach and J.I. Sperl and V. Golkov and E.T. Tan and L. Marinelli and K.F. King and C.J. Hardy and Q. Zhu and M. Czisch and P. Sämann and A. Haase and M.I. Menzel}, title = {Evaluation of {DSI} Imaging with Compressed Sensing under the Presence of Different Noise Levels on a Diffusion Phantom}, booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, year = {2012}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing}, } @inproceedings{Golkov-et-al-esmrmb12, author = {V. Golkov and J.I. Sperl and T. Sprenger and H.-J. Bungartz and M. Sedlacek and E.T. Tan and L. Marinelli and C.J. Hardy and K.F. King and M.I. Menzel}, title = {Comparison of Diffusion Kurtosis Tensor Estimation Methods in an Advanced Quality Assessment Framework}, booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, year = {2012}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging}, } @inproceedings{Gomez-et-al-ismrm15, author = {P.A. Gómez and T. Sprenger and A.A. López and J.I. Sperl and B. Fernandez and M. Molina-Romero and X. Liu and V. Golkov and M. Czisch and P. Saemann and M.I. Menzel and B.H. Menze}, title = {Using Diffusion and Structural {MRI} for the Automated Segmentation of Multiple Sclerosis Lesions}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2015}, keywords = {magnetic resonance imaging, diffusion MRI, segmentation, medical imaging}, } @inproceedings{Menzel-et-al-ismrm15, author = {M.I. Menzel and T. Sprenger and E.T. Tan and V. Golkov and C.J. Hardy and L. Marinelli and J.I. Sperl}, title = {Robustness of Phase Sensitive Reconstruction in Diffusion Spectrum Imaging}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2015}, keywords = {magnetic resonance imaging, diffusion MRI, medical imaging}, } @inproceedings{Menini-et-al-ismrm15, author = {A. Menini and V. Golkov and F. Wiesinger}, title = {Free-Breathing, Self-Navigated {RUFIS} Lung Imaging with Motion Compensated Image Reconstruction}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, year = {2015}, keywords = {magnetic resonance imaging, medical imaging}, } @inproceedings{Golkov-et-al-miccai2015-qDL, author = {V. Golkov and A. Dosovitskiy and P. Sämann and J. I. Sperl and T. Sprenger and M. Czisch and M. I. Menzel and P. A. Gómez and A. Haase and T. Brox and D. Cremers}, title = {{q-Space} Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion {MRI} Scans}, booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)}, month = {oct}, year = {2015}, address = {Munich, Germany}, keywords = {magnetic resonance imaging, diffusion MRI, deep learning, q-space deep learning, machine learning, model-free diffusion MRI, segmentation, medical imaging, deep learning}, } @inproceedings{flownet-iccv-15, author = {A. Dosovitskiy and P. Fischer and E. Ilg and P. Haeusser and C. Hazirbas and V. Golkov and P. van der Smagt and D. Cremers and T. Brox}, title = {{FlowNet: Learning Optical Flow with Convolutional Networks}}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, keywords = {deep learning, optical-flow}, year = {2015}, month = {dec}, doi = {10.1109/ICCV.2015.316}, } @incollection{Golkov-et-al-cdmri2015-holistic, author = {V. Golkov and J. M. Portegies and A. Golkov and R. Duits and D. Cremers}, title = {Holistic Image Reconstruction for Diffusion {MRI}}, booktitle = {Computational Diffusion {MRI}}, month = {oct}, publisher = {Springer}, year = {2015}, address = {Munich, Germany}, keywords = {magnetic resonance imaging, diffusion MRI, primal-dual, space of positions and orientations, medical imaging}, award = {Book Chapter, and Oral Presentation at {MICCAI} 2015 Workshop on Computational Diffusion {MRI}}, } @inproceedings{Golkov-et-al-isbi2016, author = {V. Golkov and T. Sprenger and J. I. Sperl and M. I. Menzel and M. Czisch and P. Sämann and D. Cremers}, title = {Model-Free Novelty-Based Diffusion {MRI}}, booktitle = {{IEEE} International Symposium on Biomedical Imaging ({ISBI})}, month = {apr}, year = {2016}, address = {Prague, Czech Republic}, keywords = {magnetic resonance imaging, diffusion MRI, novelty detection, q-space, machine learning, model-free diffusion MRI, segmentation, medical imaging}, } @inproceedings{Golkov-et-al-nips2016, author = {V. Golkov and M. J. Skwark and A. Golkov and A. Dosovitskiy and T. Brox and J. Meiler and D. Cremers}, title = {Protein Contact Prediction from Amino Acid Co-Evolution Using Convolutional Networks for Graph-Valued Images}, booktitle = {Annual Conference on Neural Information Processing Systems (NIPS)}, month = {dec}, year = {2016}, address = {Barcelona, Spain}, keywords = {computational structural biology, deep learning, convolutional networks, graph-valued images, deep learning, biology}, award = {Oral Presentation (acceptance rate: under 2%)}, } @article{Sprenger-et-al-mrm2016, author = {T. Sprenger and J. I. Sperl and B. Fernandez and V. Golkov and I. Eidner and P. G. Sämann and M. Czisch and E. T. Tan and C. J. Hardy and L. Marinelli and A. Haase and M. I. Menzel}, title = {Bias and Precision Analysis of Diffusional Kurtosis Imaging for Different Acquisition Schemes}, year = {2016}, journal = {Magnetic Resonance in Medicine}, } @article{Golkov-et-al-tmi2016, author = {V. Golkov and A. Dosovitskiy and J. I. Sperl and M. I. Menzel and M. Czisch and P. Sämann and T. Brox and D. Cremers}, title = {{q-Space} Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion {MRI} Scans}, year = {2016}, journal = {IEEE Transactions on Medical Imaging}, volume = {35}, issue = {5}, keywords = {magnetic resonance imaging, diffusion MRI, deep learning, q-space deep learning, machine learning, model-free diffusion MRI, segmentation, medical imaging, deep learning}, issuetitle = {Special Issue on Deep Learning}, award = {Special Issue on Deep Learning}, } @inproceedings{Peeken-et-al-2017, author = {J.C. Peeken and C. Knie and V. Golkov and K. Kessel and F. Pasa and Q. Khan and M. Seroglazov and J. Kukačka and T. Goldberg and L. Richter and J. Reeb and B. Rost and F. Pfeiffer and D. Cremers and F. Nüsslin and S.E. Combs}, title = {Establishment of an interdisciplinary workflow of machine learning-based Radiomics in sarcoma patients}, year = {2017}, booktitle = {23. Jahrestagung der Deutschen Gesellschaft für Radioonkologie (DEGRO)}, keywords = {deep learning, medical imaging}, } @inproceedings{Golkov-et-al-arxiv2017-function3d, author = {V. Golkov and M. J. Skwark and A. Mirchev and G. Dikov and A. R. Geanes and J. Mendenhall and J. Meiler and D. Cremers}, title = {{3D} Deep Learning for Biological Function Prediction from Physical Fields}, booktitle = {International Conference on 3D Vision (3DV)}, year = {2020}, journal = {arXiv preprint arXiv:1704.04039}, eprint = {1704.04039}, eprinttype = {arXiv}, keywords = {computational structural biology, deep learning, convolutional networks, protein function, QSAR, deep learning, biology}, } @article{Kukacka-et-al-2017, author = {J. Kukačka and V. Golkov and D. Cremers}, title = {Regularization for Deep Learning: A Taxonomy}, year = {2017}, journal = {arXiv preprint arXiv:1710.10686}, eprint = {1710.10686}, eprinttype = {arXiv}, keywords = {deep learning, neural networks, regularization, data augmentation, network architecture, loss function, dropout, residual learning, optimization}, } @inproceedings{Golkov-et-al-ismrm2018-novelty, author = {V. Golkov and A. Vasilev and F. Pasa and I. Lipp and W. Boubaker and E. Sgarlata and F. Pfeiffer and V. Tomassini and D. K. Jones and D. Cremers}, title = {{q-Space} Novelty Detection in Short Diffusion {MRI} Scans of Multiple Sclerosis}, year = {2018}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, keywords = {novelty detection, anomaly detection, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, segmentation}, } @inproceedings{Vasilev-et-al-2018, author = {A. Vasilev and V. Golkov and M. Meissner and I. Lipp and E. Sgarlata and V. Tomassini and D. K. Jones and D. Cremers}, title = {{q}-{S}pace Novelty Detection with Variational Autoencoders}, year = {2019}, booktitle = {{MICCAI} 2019 International Workshop on Computational Diffusion {MRI}}, eprint = {1806.02997}, eprinttype = {arXiv}, keywords = {deep learning, novelty detection, anomaly detection, neural networks, medical imaging, magnetic resonance imaging, diffusion MRI}, award = {Oral Presentation}, } @inproceedings{Swazinna-et-al-ismrm2019, author = {P. Swazinna and V. Golkov and I. Lipp and E. Sgarlata and V. Tomassini and D. K. Jones and D. Cremers}, title = {Negative-Unlabeled Learning for Diffusion {MRI}}, year = {2019}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, keywords = {deep learning, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, weakly-supervised learning, positive-unlabeled learning, semi-supervised learning, localization}, } @inproceedings{Golkov-et-al-ismrm2018-global, author = {V. Golkov and P. Swazinna and M. M. Schmitt and Q. A. Khan and C. M. W. Tax and M. Serahlazau and F. Pasa and F. Pfeiffer and G. J. Biessels and A. Leemans and D. Cremers}, title = {{q-Space} Deep Learning for {A}lzheimer's Disease Diagnosis: Global Prediction and Weakly-Supervised Localization}, year = {2018}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, keywords = {deep learning, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, weakly-supervised learning, localization}, } @article{Do-et-al-2018-pre-miRNA, author = {B. T. Do and V. Golkov and G. E. Gürel and D. Cremers}, title = {Precursor {microRNA} Identification Using Deep Convolutional Neural Networks}, year = {2018}, % howpublished = {Preprint}, journal = {bioRxiv preprint 414656}, keywords = {precursor microRNA, pre-miRNA, miRNA, deep learning, neural networks, machine learning, biology, gene regulation}, } @article{Golkov-et-al-2020-ROC, author = {V. Golkov and A. Becker and D. T. Plop and D. Čuturilo and N. Davoudi and J. Mendenhall and R. Moretti and J. Meiler and D. Cremers}, title = {Deep Learning for Virtual Screening: Five Reasons to Use {ROC} Cost Functions}, year = {2020}, journal = {arXiv preprint arXiv:2007.07029}, eprint = {2007.07029}, eprinttype = {arXiv}, keywords = {deep learning, drug discovery, virtual screening, neural networks, machine learning, QSAR, classification, receiver operating characteristic, biology, chemistry}, } @article{Aljalbout-et-al-2018, author = {E. Aljalbout and V. Golkov and Y. Siddiqui and M. Strobel and D. Cremers}, title = {Clustering with Deep Learning: Taxonomy and New Methods}, year = {2018}, journal = {arXiv preprint arXiv:1801.07648}, eprint = {1801.07648}, eprinttype = {arXiv}, keywords = {deep learning, clustering, cluster analysis, neural networks}, } @inproceedings{haeusser18associative, author = {P. Haeusser and J. Plapp and V. Golkov and E. Aljalbout and D. Cremers}, title = {Associative Deep Clustering - Training a Classification Network with no Labels}, booktitle = {Proc. of the German Conference on Pattern Recognition (GCPR)}, year = {2018}, month = {October}, titleurl = {haeusser18associative.pdf}, keywords = {Clustering, Embeddings, deep learning, associative_learning}, } @article{Pasa-et-al-2019, author = {F. Pasa and V. Golkov and F. Pfeiffer and D. Cremers and D. Pfeiffer}, title = {Efficient Deep Network Architectures for Fast Chest {X}-Ray Tuberculosis Screening and Visualization}, journal = {Scientific Reports}, year = {2019}, volume = {9}, number = {1}, pages = {6268}, issn = {2045-2322}, doi = {10.1038/s41598-019-42557-4}, url = {https://www.nature.com/articles/s41598-019-42557-4}, keywords = {medical imaging, deep learning}, } @article{Schuchardt-et-al-2019, author = {J. Schuchardt and V. Golkov and D. Cremers}, title = {Learning to Evolve}, year = {2019}, journal = {arXiv preprint arXiv:1905.03389}, eprint = {1905.03389}, eprinttype = {arXiv}, keywords = {evolutionary algorithms, evolutionary computation, genetic algorithms, deep learning, neural networks, reinforcement learning}, } @article{Della-Libera-et-al-2019, author = {L. Della Libera and V. Golkov and Y. Zhu and A. Mielke and D. Cremers}, title = {Deep Learning for {2D and 3D} Rotatable Data: An Overview of Methods}, year = {2019}, journal = {arXiv preprint arXiv:1910.14594}, eprint = {1910.14594}, eprinttype = {arXiv}, keywords = {deep learning, neural networks, 2D, 3D, rotations, invariance, equivariance}, } @article{Naeyaert2020, author = {M. Naeyaert and J. Aelterman and J. Van Audekerke and V. Golkov and D. Cremers and A. Pižurica and J. Sijbers and M. Verhoye}, title = {Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing}, journal = {Magnetic Resonance in Medicine}, year = {2020}, volume = {85}, number = {3}, pages = {1397-1413}, keywords = {compressed sensing, diffusion, fast spin echo, HARDI, turbo spin echo, medical imaging, diffusion MRI}, doi = {10.1002/mrm.28520}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28520}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.28520}, } @article{fabbro2020, title = {Speech Synthesis and Control Using Differentiable {DSP}}, author = {G Fabbro and V Golkov and T Kemp and D Cremers}, year = {2020}, journal = {arXiv preprint arXiv:2010.15084}, eprint = {2010.15084}, eprinttype = {arXiv}, primaryclass = {eess.AS}, keywords = {speech synthesis, neural vocoder, text-to-speech, digital signal processing, neural networks, deep learning}, } @article{mueller2021, title = {Rotation-Equivariant Deep Learning for Diffusion {MRI}}, author = {P. Müller and V. Golkov and V. Tomassini and D. Cremers}, year = {2021}, journal = {arXiv preprint}, eprint = {2102.06942}, eprinttype = {arXiv}, primaryclass = {cs.CV}, keywords = {deep learning, diffusion MRI, equivariant deep learning, rotation-equivariance, magnetic resonance imaging, multiple sclerosis, image segmentation, medical imaging}, } @inproceedings{Naeyaert2021, author = {M Naeyaert and V Golkov and D Cremers and J Sijbers and M Verhoye}, title = {Faster and better {HARDI} using {FSE} and holistic reconstruction}, year = {2021}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, keywords = {compressed sensing, magnetic resonance imaging, diffusion MRI, fast spin echo, HARDI, turbo spin echo, primal-dual, space of positions and orientations, medical imaging, image reconstruction, inverse problems}, } @inproceedings{Mueller2021-ISMRM, title = {Rotation-Equivariant Deep Learning for Diffusion {MRI} (short version)}, author = {P. Müller and V. Golkov and V. Tomassini and D. Cremers}, year = {2021}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, keywords = {deep learning, diffusion MRI, equivariant deep learning, rotation-equivariance, magnetic resonance imaging, multiple sclerosis, image segmentation, medical imaging}, } @article{mozes2021, title = {Scene Graph Generation for Better Image Captioning?}, author = {M. Mozes and M. Schmitt and V. Golkov and H. Schütze and D. Cremers}, year = {2021}, journal = {arXiv preprint}, eprint = {2109.11398}, eprinttype = {arXiv}, primaryclass = {cs.CV}, keywords = {deep learning, computer vision, natural language processing, image captioning, scene graphs, attention mechanism}, } @phdthesis{Golkov-PhDthesis, author = {V. Golkov}, title = {Deep learning and variational analysis for high-dimensional and geometric biomedical data}, school = {Department of Informatics, Technical University of Munich, Germany}, year = {2021}, topic = {deep learning, medical imaging, protein structure prediction, neural networks, machine learning, calculus of variations, diffusion MRI, magnetic resonance imaging}, keywords = {deep learning, medical imaging, protein structure prediction, neural networks, machine learning, calculus of variations, diffusion MRI, magnetic resonance imaging}, } @inproceedings{Veraart2022, title = {A data-driven variability assessment of brain diffusion {MRI} preprocessing pipelines}, author = {J. Veraart and 100 coauthors}, year = {2022}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, keywords = {diffusion MRI, magnetic resonance imaging, medical imaging}, award = {Oral Presentation}, } @article{Brunner2022, author = {C. Brunner and A. Duensing and C. Schröder and M. Mittermair and V. Golkov and M. Pollanka and D. Cremers and R. Kienberger}, title = {Deep Learning in Attosecond Metrology}, journal = {Optics Express}, year = {2022}, keywords = {attosecond metrology, photoelectron spectroscopy, deep learning, physics, attosecond streak camera, streaking, Neural networks; Phase retrieval; Photoelectron spectra; Power spectra; Streak cameras; Time resolved spectroscopy}, volume = {30}, number = {9}, pages = {15669--15684}, publisher = {OSA}, url = {https://opg.optica.org/OE/abstract.cfm?uri=OE-30-9-15669}, doi = {10.1364/OE.452108}, award = {Editor's Pick}, } @article{Wimmer2023, title = {Scale-Equivariant Deep Learning for 3D Data}, author = {T Wimmer and V Golkov and HN Dang and M Zaiss and A Maier and D Cremers}, year = {2023}, journal = {arXiv preprint}, eprint = {2304.05864}, eprinttype = {arXiv}, primaryclass = {cs.CV}, keywords = {deep learning, equivariant deep learning, scale-equivariance, magnetic resonance imaging, image segmentation, medical imaging}, } @article{dang2023, title = {Joint {MR} sequence optimization beats pure neural network approaches for spin-echo {MRI} super-resolution}, author = {HN Dang and V Golkov and T Wimmer and D Cremers and A Maier and M Zaiss}, journal = {arXiv preprint arXiv:2305.07524}, year = {2023}, keywords = {medical imaging, magnetic resonance imaging, pulse sequences, super-resolution, deep learning}, eprint = {2305.07524}, eprinttype = {arXiv}, } @inproceedings{zaiss2023, title = {{GPT4MR}: Exploring {GPT-4} as an {MR} Sequence and Reconstruction Programming Assistant}, author = {M Zaiss and HN Dang and V Golkov and J Rajput and D Cremers and F Knoll and A Maier}, year = {2023}, keywords = {medical imaging, magnetic resonance imaging, pulse sequences, deep learning, large language models, prompt engineering}, url = {https://docs.google.com/document/d/1iy6AaTWCpGjfVc5Z0ar7VXWyJyNq3PlY2NYoeKCD0T4/}, booktitle = {European Society for Magnetic Resonance in Medicine and Biology ({ESMRMB}) Annual Meeting}, award = {Oral Presentation}, }