@proceedings {7031,
	title = {Shape or Texture: Understanding Discriminative Features in CNNs},
	year = {2021},
	author = {Md Amirul Islam and Matthew Kowal and Patrick Esser and Sen Jia and Bj{\"o}rn Ommer and Konstantinos G Derpanis and Neil Bruce}
}
@conference {Cvpr2020,
	title = {Self-Supervised Viewpoint Learning From Image Collections},
	booktitle = {CONSAC},
	year = {2020},
	abstract = {Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets. However, manually labeling viewpoints is notoriously hard, error-prone, and time-consuming. On the other hand, it is relatively easy to mine many unlabelled images of an object category from the internet, e.g., of cars or faces. We seek to answer the research question of whether such un-labeled collections of in-the-wild images can be successfully utilized to train viewpoint estimation networks for general object categories purely via self-supervision. Self-supervision here refers to the fact that the only true supervisory signal that the network has is the input image itself. We propose a novel learning framework which incorporates an analysis-by-synthesis paradigm to reconstruct images in a viewpoint aware manner with a generative network, along with symmetry and adversarial constraints to successfully supervise our viewpoint estimation network. We show that our approach performs competitively to fully-supervised approaches for several object categories like human faces, cars, buses, and trains. Our work opens up further research in self-supervised viewpoint learning and serves as a robust baseline for it. We open-source our code at https://github.com/NVlabs/SSV .},
	url = {https://github.com/NVlabs/SSV},
	author = {S.K. Mustikovela and V. Jampani and S. De Mello and S. Liu and U. Iqbal and C. Rother and J. Kautz}
}
@article {Kleesiek2019,
	title = {Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study},
	journal = {Investigative Radiology},
	volume = {54},
	year = {2019},
	pages = {653{\textendash}660},
	abstract = {Objectives Gadolinium-based contrast agents (GBCAs) have become an integral part in daily clinical decision making in the last 3 decades. However, there is a broad consensus that GBCAs should be exclusively used if no contrast-free magnetic resonance imaging (MRI) technique is available to reduce the amount of applied GBCAs in patients. In the current study, we investigate the possibility of predicting contrast enhancement from noncontrast multiparametric brain MRI scans using a deep-learning (DL) architecture. Materials and Methods A Bayesian DL architecture for the prediction of virtual contrast enhancement was developed using 10-channel multiparametric MRI data acquired before GBCA application. The model was quantitatively and qualitatively evaluated on 116 data sets from glioma patients and healthy subjects by comparing the virtual contrast enhancement maps to the ground truth contrast-enhanced T1-weighted imaging. Subjects were split in 3 different groups: Enhancing tumors (n = 47), nonenhancing tumors (n = 39), and patients without pathologic changes (n = 30). The tumor regions were segmented for a detailed analysis of subregions. The influence of the different MRI sequences was determined. Results Quantitative results of the virtual contrast enhancement yielded a sensitivity of 91.8\% and a specificity of 91.2\%. T2-weighted imaging, followed by diffusion-weighted imaging, was the most influential sequence for the prediction of virtual contrast enhancement. Analysis of the whole brain showed a mean area under the curve of 0.969 {\textpm} 0.019, a peak signal-to-noise ratio of 22.967 {\textpm} 1.162 dB, and a structural similarity index of 0.872 {\textpm} 0.031. Enhancing and nonenhancing tumor subregions performed worse (except for the peak signal-to-noise ratio of the nonenhancing tumors). The qualitative evaluation by 2 raters using a 4-point Likert scale showed good to excellent (3-4) results for 91.5\% of the enhancing and 92.3\% of the nonenhancing gliomas. However, despite the good scores and ratings, there were visual deviations between the virtual contrast maps and the ground truth, including a more blurry, less nodular-like ring enhancement, few low-contrast false-positive enhancements of nonenhancing gliomas, and a tendency to omit smaller vessels. These "features" were also exploited by 2 trained radiologists when performing a Turing test, allowing them to discriminate between real and virtual contrast-enhanced images in 80\% and 90\% of the cases, respectively. Conclusions The introduced model for virtual gadolinium enhancement demonstrates a very good quantitative and qualitative performance. Future systematic studies in larger patient collectives with varying neurological disorders need to evaluate if the introduced virtual contrast enhancement might reduce GBCA exposure in clinical practice.},
	keywords = {Bayesian uncertainty, deep learning, gadolinium-based contrast agents, glioma, multiparametric MRI},
	issn = {15360210},
	doi = {10.1097/RLI.0000000000000583},
	author = {Kleesiek, Jens and Morshuis, Jan Nikolas and Isensee, Fabian and Deike-Hofmann, Katerina and Paech, Daniel and Kickingereder, Philipp and K{\"o}the, Ullrich and Rother, Carsten and Forsting, Michael and Wick, Wolfgang and Bendszus, Martin and Schlemmer, Heinz Peter and Radbruch, Alexander}
}
@article {7083,
	title = {Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures},
	journal = {Nature Communications},
	volume = {13;10(1)},
	year = {2019},
	doi = {10.1038/s41467-019-09879-3},
	author = {Imle, A and Kumberger, P and Schnellb{\"a}cher, ND and Fehr, J and Carillo-Bustamente, P and Ales, J and Schmidt, P and Ritter, C and Godinez, WJ and M{\"u}ller, B and Rohr, K and Hamprecht, FA and Schwarz, US and Graw, F and Fackler, OT}
}
@conference {Hodan2018,
	title = {BOP: Benchmark for 6D object pose estimation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11214 LNCS},
	year = {2018},
	month = {aug},
	pages = {19{\textendash}35},
	abstract = {We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: (i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, (ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, (iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and (iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at bop.felk.cvut.cz.},
	isbn = {9783030012489},
	issn = {16113349},
	doi = {10.1007/978-3-030-01249-6_2},
	url = {http://arxiv.org/abs/1808.08319},
	author = {Hoda{\v n}, Tom{\'a}{\v s} and Michel, Frank and Brachmann, Eric and Kehl, Wadim and Buch, Anders Glent and Kraft, Dirk and Drost, Bertram and Vidal, Joel and Ihrke, Stephan and Zabulis, Xenophon and Sahin, Caner and Manhardt, Fabian and Tombari, Federico and Kim, Tae Kyun and Matas, Ji{\v r}{\'\i} and Carsten Rother}
}
@article {6254,
	title = {Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats},
	journal = {Sage Journals},
	volume = { Journal of Cerebral Blood Flow \& Metabolism},
	year = {2018},
	month = {05/18},
	abstract = {The majority of stroke patients develop post-stroke fatigue, a symptom which impairs motivation and diminishes the success of rehabilitative interventions. We show that large cortical strokes acutely reduce activity levels in rats for 1{\textendash}2 weeks as a physiological response paralleled by signs of systemic inflammation. Rats were exposed early (1{\textendash}2 weeks) or late (3{\textendash}4 weeks after stroke) to an individually monitored enriched environment to stimulate self-controlled high-intensity sensorimotor training. A group of animals received Anti-Nogo antibodies for the first two weeks after stroke, a neuronal growth promoting immunotherapy already in clinical trials. Early exposure to the enriched environment resulted in poor outcome: Training intensity was correlated to enhanced systemic inflammation and functional impairment. In contrast, animals starting intense sensorimotor training two weeks after stroke preceded by the immunotherapy revealed better recovery with functional outcome positively correlated to the training intensity and the extent of re-innervation of the stroke denervated cervical hemi-cord. Our results suggest stroke-induced fatigue as a biological purposeful reaction of the organism during neuronal remodeling, enabling new circuit formation which will then be stabilized or pruned in the subsequent rehabilitative training phase. However, intense training too early may lead to wrong connections and is thus less effective.},
	url = {http://journals.sagepub.com/doi/abs/10.1177/0271678X18777661},
	author = {Anna-Sophia Wahl and Erlebach, E. and Biagio Brattoli and Uta B{\"u}chler and Kaiser, J. and Ineichen, V. B. and Alice C. Mosberger and Schneeberger, S. and Imobersteg, S. and Wieckhorst, M. and Stirn, M. and Schroeter, A. and Bj{\"o}rn Ommer and M. E. Schwab}
}
@conference {Levinkov2017,
	title = {Joint graph decomposition \& node labeling: Problem, algorithms, applications},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	pages = {1904{\textendash}1912},
	abstract = {We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. This problem offers a common mathematical abstraction of seemingly unrelated computer vision tasks, including instance-separating semantic segmentation, articulated human body pose estimation and multiple object tracking. Conceptually, the problem we state generalizes the unconstrained integer quadratic program and the minimum cost lifted multicut problem, both of which are NP-hard. In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time. To demonstrate their effectiveness in tackling computer vision tasks, we apply these algorithms to instances of the problem that we construct from published data, using published algorithms. We report state-of-the-art application-specific accuracy for the three above-mentioned applications.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.206},
	author = {Levinkov, Evgeny and Uhrig, Jonas and Tang, Siyu and Omran, Mohamed and Insafutdinov, Eldar and Kirillov, Alexander and Carsten Rother and Brox, Thomas and Schiele, Bernt and Bj{\"o}rn Andres}
}
@article {6188,
	title = {Optogenetically stimulating the intact corticospinal tract post-stroke restores motor control through regionalized functional circuit formation},
	journal = {Nature Communications},
	year = {2017},
	pages = {(ASW \& UB contributed equally; BO and MES contributed equally)},
	doi = {10.1038/s41467-017-01090-6},
	url = {https://www.nature.com/articles/s41467-017-01090-6},
	author = {Anna-Sophia Wahl and Uta B{\"u}chler and A. Br{\"a}ndli and Biagio Brattoli and S. Musall and H. Kasper and B.V. Ineichen and F. Helmchen and Bj{\"o}rn Ommer and M. E. Schwab}
}
@article {zechmann_12_automated,
	title = {Automated vs. manual pattern recognition of 3D 1H MRSI data of patients with prostate cancer},
	journal = {Academic Radiology},
	volume = {19, 6},
	year = {2012},
	pages = {675-684},
	doi = {10.1016/j.acra.2012.02.014},
	author = {C. M. Zechmann and Bjoern H. Menze and B. Michael Kelm and Zamecnik, P. and Ikinger, U. and Waldherr, R. and Delorme, S. and Fred A. Hamprecht and Bachert, P.}
}
@conference {kondermann_12_on,
	title = {On Performance Analysis of Optical Flow Algorithms},
	booktitle = {Outdoor and Large-Scale Real-Worls Scene Analysis, Dagstuhl-Workshop
2011},
	volume = {LNCS},
	number = {7474},
	year = {2012},
	pages = {329-355},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-34091-8_15},
	author = {Daniel Kondermann and Steffen Abraham and W. F{\"o}rstner and Gehrig, S. and Imiya, A. and Bernd J{\"a}hne and Klose, F. and Magor, M. and Helmut Mayer and Mester, R. and Pajdla, T. and Reulke, R. and Zimmer, H.}
}
@conference {meister_12_when,
	title = {When Can We Use KinectFusion for Ground Truth Acquisition?},
	booktitle = {Workshop on Color-Depth Camera Fusion in Robotics, IEEE International
Conference on Intelligent Robots and Systems},
	year = {2012},
	note = {1},
	author = {Stephan Meister and Izadi, S. and Kohli, P. and H{\"a}mmerle, M. and Carsten Rother and Daniel Kondermann}
}
@article {Meister2012,
	title = {When can we use KinectFusion for ground truth acquisition?},
	journal = {Proc Workshop on \ldots},
	year = {2012},
	pages = {3{\textendash}8},
	abstract = {Abstract{\textemdash}KinectFusion is a method for real-time capture of dense 3D geometry of the physical environment using a depth sensor. The system allows capture of a large dataset of 3D scene reconstructions at very low cost. In this paper we discuss the properties of the ...$\backslash$n},
	url = {http://meshlab.sourceforge.net/ http://www.msr-waypoint.net/en-us/um/people/pkohli/papers/mikhrk_iros_dataset_2012.pdf\%5Cnpapers3://publication/uuid/2615CF9D-C632-4E39-B1C4-B32A4A5D339C},
	author = {Meister, Stephan and Izadi, Shahram and Kohli, Pushmeet and M H{\"a}mmerle}
}
@article {renard_09_when,
	title = {When Less Can Yield More - Computational Preprocessing of MS/MS Spectra for Peptide Identification Preprocessing},
	journal = {Proteomics},
	volume = {9},
	year = {2009},
	note = {1},
	pages = {4978-4984},
	doi = {10.1002/pmic.200900326},
	author = {B. Y. Renard and Kirchner, M. and Monigatti, F. and Ivanov, A. R. and Rappsilber, J. and Winter, D. and Judith A. J. Steen and Fred A. Hamprecht and Steen, H.}
}
@article {weber_07_comparison,
	title = {Comparison of correctness of manuel and automatic evaluation of MR-spectrum with prostrate cancer},
	journal = {Der Urologe},
	volume = {46},
	number = {9},
	year = {2007},
	pages = {1252},
	doi = {10.1007/s00120-007-1488-1},
	author = {C. Weber and C. M. Zechmann and B. Michael Kelm and Zamecnik, R. and Hendricks, D. and Waldherr, R. and Fred A. Hamprecht and Delorme, S. and Bachert, P. and Ikinger, U.}
}
@conference {zechmann_06_can,
	title = {Can man still beat the machine? Automated vs. manual pattern recognition of 3D MRSI data of prostate cancer patients},
	booktitle = {Proceedings of the 16th ISMRM},
	year = {2006},
	author = {C. M. Zechmann and B. Michael Kelm and Zamecnik, P. and Ikinger, U. and Waldherr, R. and R{\"o}ll, S. and Delorme, S. and Fred A. Hamprecht and Bachert, P.}
}
@conference {jaehne1984a,
	title = {Parameterization of air/lake gas exchange},
	booktitle = {Gas transfer at water surfaces},
	year = {1984},
	pages = {469--476},
	publisher = {Reidel},
	organization = {Reidel},
	doi = {10.1007/978-94-017-1660-4_42},
	author = {Bernd J{\"a}hne and K. H. Fischer and Johann Ilmberger and P. Libner and W. Weiss and D. Imboden and U. Lemnin and J. M. Jaquet},
	editor = {W. Brutsaert and G. H. Jirka}
}
@conference {jaehne1984b,
	title = {Wind/wave-tunnel experiments on the Schmidt number and wave field dependence of air-water gas exchange},
	booktitle = {Gas transfer at water surfaces},
	year = {1984},
	pages = {303--309},
	publisher = {Reidel},
	organization = {Reidel},
	doi = {10.1007/978-94-017-1660-4_28},
	author = {Bernd J{\"a}hne and Werner A. Huber and A. Dutzi and Thomas Wais and Johann Ilmberger},
	editor = {W. Brutsaert and G. H. Jirka}
}
@article {lange1982,
	title = {Comparison between an amplitude-measuring wire and a slope-measuring laser water wave gauge},
	journal = {Rev. Sci. Instrum.},
	volume = {53},
	number = {5},
	year = {1982},
	pages = {651--655},
	abstract = {Capillary waves produced in a laboratory wind wave tunnel have been measured using a wire resistance-type gauge (measuring wave amplitude) and a laser gauge (measuring wave slope). Comparison of power spectra of the gauges shows good agreement to 80 Hz, which is the upper frequency limit of the wire gauge. The upper frequency limit of the laser gauge depends upon laser beam diameter and is about 300 Hz.},
	doi = {10.1063/1.1137036},
	author = {Lange, P. A. and Bernd J{\"a}hne and Tschiersch, J. and Johann Ilmberger}
}
@mastersthesis {ilmberger1981,
	title = {Impuls{\"u}bertrag und Str{\"o}mungsverh{\"a}ltnisse in einem runden Wind-Wasser Kanal},
	year = {1981},
	note = {IUP D-167},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	doi = {10.5281/zenodo.13346},
	author = {Johann Ilmberger}
}
