% generated by bibtexbrowser % % Encoding: UTF-8 @inproceedings{SSKKC-10, author = {M. Schikora and A. Schikora and K.-H. Kogel and W. Koch and D. Cremers}, title = {Probabilistic Classification of Disease Symptoms caused by Salmonella on Arabidopsis Plants}, month = {September}, year = {2010}, booktitle = {5th IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF) }, address = {Leipzig, Germany}, titleurl = {2010_sskkc_sdf.pdf}, keywords = {biology}, } @inproceedings{Pock-et-al-09, author = {T. Pock and A. Chambolle and H. Bischof and D. Cremers}, title = {A Convex Relaxation Approach for Computing Minimal Partitions}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2009}, address = {Miami, Florida}, titleurl = {pock_et_al_cvpr09.pdf}, topic = {Convex Relaxation Methods}, keywords = {convex-relaxation, medical imaging}, } @inproceedings{Pock-et-al-cvpr08-ws, author = {T. Pock and M. Unger and D. Cremers and H. Bischof}, title = {Fast and Exact Solution of Total Variation Models on the GPU}, optcrossref = {}, optkey = {}, booktitle = {CVPR Workshop on Visual Computer Vision on GPU's}, optpages = {}, year = {2008}, opteditor = {}, optvolume = {}, optnumber = {}, optseries = {}, optaddress = {Anchorage, Alaska}, month = {June}, optorganization = {}, optpublisher = {}, titleurl = {cvpr2008ws.pdf}, optannote = {}, keywords = {medical imaging}, } @incollection{Klodt-et-al-13, author = {M. Klodt and F. Steinbruecker and D. Cremers}, title = {Moment Constraints in Convex Optimization for Segmentation and Tracking}, booktitle = {Advanced Topics in Computer Vision}, publisher = {Springer}, year = {2013}, titleurl = {Klodt-et-al-13.pdf}, keywords = {segmentation, convex-relaxation, medical imaging}, topic = {Convex Relaxation Methods, Segmentation}, } @article{Cremers-et-al-ijcv07, author = {D. Cremers and M. Rousson and R. Deriche}, title = {A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape}, journal = {International Journal of Computer Vision}, year = {2007}, volume = {72}, number = {2}, pages = {195--215}, month = {apr}, titleurl = {cremers_rousson_deriche_ijcv07.pdf}, keywords = {shape-priors, medical imaging}, topic = {Segmentation, Statistics, Shape Priors, Level Sets, Motion}, } @inproceedings{Cremers-et-al-06c, author = {D. Cremers and C. Guetter and C. Xu}, title = {Nonparametric priors on the space of joint intensity distributions for non-rigid multi-modal image registration}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2006}, month = {June}, pages = {1777--1783}, volume = {2}, titleurl = {cremers_et_al_cvpr06.pdf}, topic = {Optic Flow, Medical Image Analysis, Correspondence, Motion}, keywords = {medical imaging}, } @article{Cremers-Herz-02, author = {D. Cremers and A. V. M. Herz}, title = {Travelling waves of exitation in neural field models: {E}quivalence of rate descriptions and integrate-and-fire dynamics}, journal = {Neural Computation}, year = {2002}, volume = {14}, pages = {1651--1667}, number = {7}, titleurl = {nc_02.pdf}, topic = {Neural Field Models}, keywords = {biology}, } @article{Cremers-et-al-06b, author = {D. Cremers and S. J. Osher and S. Soatto}, title = {Kernel density estimation and intrinsic alignment for shape priors in level set segmentation}, journal = {International Journal of Computer Vision}, year = {2006}, volume = {69}, number = {3}, month = {sep}, pages = {335--351}, titleurl = {cremers_osher_soatto_ijcv06.pdf}, topic = {Level Sets, Shape Priors, Statistics, Segmentation}, keywords = {medical imaging}, } @incollection{Cremers-Rousson-07, author = {D. Cremers and M. Rousson}, title = {Efficient kernel density estimation of shape and intensity priors for level set segmentation}, booktitle = {Parametric and {G}eometric {D}eformable {M}odels: {A}n application in {B}iomaterials and {M}edical {I}magery}, publisher = {Springer}, year = {2007}, editor = {J. S. Suri and A. Farag}, month = {May}, titleurl = {cremers_rousson07.pdf}, topic = {Segmentation, Shape Priors, Medical Image Analysis, Statistics}, keywords = {shape-priors, medical imaging}, } @article{Chen-et-al-ivc12, author = {S. Chen and D. Cremers and R. J. Radke}, title = {Image segmentation with one shape prior - A template-based formulation}, journal = {Image and Vision Computing}, year = {2012}, volume = {30}, number = {12}, pages = {1032--1042}, titleurl = {chen-et-al-ivc12.pdf}, topic = {Shape, Segmentation}, keywords = {medical imaging}, } @inproceedings{Rousson-Cremers-05, author = {M. Rousson and D. Cremers}, title = {Efficient kernel density estimation of shape and intensity priors for level set segmentation}, booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)}, year = {2005}, volume = {1}, pages = {757--764}, keywords = {image-segmentation,shape,Parzen,parametric,2d cardiac ultrasound,3d ct prostate, medical imaging}, titleurl = {rousson_cremers05.pdf}, topic = {Shape, Segmentation, Statistics, Level Sets}, } @inproceedings{Cremers-et-al-07, author = {D. Cremers and O. Fluck and M. Rousson and S. Aharon}, title = {A probabilistic level set formulation for interactive organ segmentation}, booktitle = {Proc. of the SPIE Medical Imaging}, year = {2007}, month = {feb}, address = {San Diego, USA}, editors = {E. Krupinski and A. Amini and M. Sonka}, keywords = {image segmentation, Parzen, Level Sets, medical imaging}, titleurl = {cremers_et_al_spie07.pdf}, topic = {Segmentation, Statistics, Level Sets}, } @inproceedings{Kohlberger-et-al-06, author = {T. Kohlberger and D. Cremers and M. Rousson and R. Ramaraj}, title = {4D shape priors for level set segmentation of the left myocardium in {SPECT} sequences}, booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)}, volume = {4190}, series = {LNCS}, pages = {92--100}, year = {2006}, month = {oct}, keywords = {image-segmentation,shape,Parzen,parametric,2d cardiac ultrasound,3d ct prostate, medical imaging}, titleurl = {kohlberger_et_al_miccai06.pdf}, topic = {Shape, Segmentation, Medical Image Analysis, Level Sets}, } @techreport{souiai_tr_13, author = {M. Souiai and E. Strekalovskiy and C. Nieuwenhuis and D. Cremers}, title = {Label Configuration Priors for Continuous Multi-Label Optimization}, type = {Technical report}, school = {Computer Vision Group, TU Munich}, titleurl = {souiai_tr_13.pdf}, year = {2013}, topic = {Convex Relaxation Methods,Image Segmentation}, keywords = {convex-relaxation,label configuration priors,medical imaging}, } @article{Kolev-et-al-PR11, author = {K. Kolev and N. Kirchgessner and S. Houben and A. Csiszar and W. Rubner and C. Palm and B. Eiben and R. Merkel and D. Cremers}, title = {A Variational Approach to Vesicle Membrane Reconstruction from Fluorescence Imaging}, journal = {Pattern Recognition}, volume = {44}, pages = {2944--2958}, year = {2011}, titleurl = {kolev_pr11.pdf}, keywords = {3d-reconstruction, medical imaging, biology}, } @inproceedings{KC11:iccv, author = {M. Klodt and D. Cremers}, title = {A Convex Framework for Image Segmentation with Moment Constraints}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2011}, titleurl = {kc11_iccv.pdf}, topic = {segmentation, Convex Relaxation Methods}, keywords = {segmentation, convex-relaxation, medical imaging}, } @inproceedings{MSKC-11, author = {S. Madhogaria and M. Schikora and W. Koch and D. Cremers}, title = {Pixel-based Classification Method for Detecting Unhealthy Regions in Leaf Images}, month = {September}, year = {2011}, booktitle = {6th IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF) }, address = {Berlin, Germany}, titleurl = {2011_MSKC_SDF.pdf}, keywords = {biology}, } @article{2012_SNMKCHKS_BIOINF, author = {M. Schikora and B. Neupane and S. Madhogaria and W. Koch and D. Cremers and H. Hirt and K.-H. Kogel and A. Schikora}, journal = {BMC Bioinformatics}, month = {July}, number = {171}, title = {An image classification approach to analyze the suppression of plant immunity by the human pathogen Salmonella Typhimurium}, volume = {13}, year = {2012}, titleurl = {2012_SNMKCHKS_BIOINF.pdf}, keywords = {biology}, } @article{Klodt-et-al-bmc15, author = {M. Klodt and K. Herzog and R. Töpfer and D. Cremers}, journal = {BMC Bioinformatics}, month = {May}, number = {143}, title = {Field phenotyping of grapevine growth using dense stereo reconstruction}, volume = {16}, year = {2015}, keywords = {biology}, } @article{nieuwenhuis-cremers-pami12_2, author = {C. Nieuwenhuis and D. Cremers}, title = {Spatially Varying Color Distributions for Interactive Multi-Label Segmentation}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2013}, volume = {35}, number = {5}, pages = {1234-1247}, titleurl = {nieuwenhuis-cremers-pami12_2.pdf}, topic = {Segmentation}, keywords = {convex-relaxation, segmentation, medical imaging}, } @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{stuehmer-et-al-iccv2013, author = {J. Stühmer and P. Schröder and D. Cremers}, title = {Tree Shape Priors with Connectivity Constraints using Convex Relaxation on General Graphs}, year = {2013}, address = {Sydney, Australia}, month = {December}, titleurl = {stuehmer-et-al-iccv2013.pdf}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, topic = {Segmentation, Shape Priors}, keywords = {Convex-Relaxation, Segmentation, shape-priors, medical imaging}, award = {Oral Presentation}, } @inproceedings{Stuehmer-Cremers-emmcvpr15, author = {J. Stühmer and D. Cremers}, title = {A Fast Projection Method for Connectivity Constraints in Image Segmentation}, booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, editor = {X.-C. Tai and E. Bae and T. F. Chan and M. Lysaker}, series = {LNCS}, year = {2015}, keywords = {image-segmentation,constrained convex optimization,shape-priors,medical imaging}, topic = {Segmentation, Shape Priors}, } @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}, } @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{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{krieg2017genetic, title = {Genetic defects in ß-spectrin and tau sensitize C. elegans axons to movement-induced damage via torque-tension coupling}, author = {M. Krieg and J. Stühmer and J. G. Cueva and R. Fetter and K. Spilker and D. Cremers and K. Shen and A. R. Dunn and M. B. Goodman}, journal = {eLife}, volume = {6}, pages = {e20172}, year = {2017}, publisher = {eLife Sciences Publications Limited}, keywords = {biology}, } @article{krieg2017tau, title = {Tau Like Proteins Reduce Torque Generation in Microtubule Bundles}, author = {M. Krieg and J. Stühmer and J. G. Cueva and R. Fetter and K. Spilker and D. Cremers and K. Shen and A. R. Dunn and M. B. Goodman}, journal = {Biophysical Journal}, volume = {112}, number = {3}, pages = {29a--30a}, year = {2017}, publisher = {Elsevier}, keywords = {biology}, } @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{roy2019noninvasive, title = {A Non-invasive {3D} Body Scanner and Software Tool towards Analysis of Scoliosis}, author = {S. Roy and A.T.D. Gruenwald and A. Alves-Pinto and R. Maier and D. Cremers and D. Pfeiffer and R. Lampe}, journal = {BioMed Research International (BMRI)}, year = {2019}, month = {May}, keywords = {rgb-d,reconstruction,3d-reconstruction,3d-scanning,medical,medical imaging}, } @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{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{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}, } @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{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 (12-page version)}, 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}, } @inproceedings{Dang2024, title = {Joint sequence optimization beats pure neural network approaches for super-resolution {TSE}}, author = {HN Dang and V Golkov and J Endres and S Weinmüller and F Glang and T Wimmer and D Cremers and A Dörfler and A Maier and and M Zaiss}, year = {2024}, booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting}, keywords = {MRI, magnetic resonance imaging, medical imaging, pulse sequences, deep learning, neural networks, MRI reconstruction}, } @inproceedings{Liebeskind2024, title = {The {Pulseq-CEST} Library: Definition of Preparations and Simulations, Example Data, and Example Evaluations}, author = {A Liebeskind and MS Fabian and JR Schüre and S Weinmüller and P Schünke and V Golkov and D Cremers and M Zaiss}, year = {2024}, booktitle = {10th International Workshop on Chemical Exchange Saturation Transfer (CEST 2024)}, keywords = {MRI, magnetic resonance imaging, medical imaging, pulse sequences, MRI reconstruction}, award = {Oral Presentation}, }