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2019
J. Kleesiek, Morshuis, J. Nikolas, Isensee, F., Deike-Hofmann, K., Paech, D., Kickingereder, P., Köthe, U., Rother, C., Forsting, M., Wick, W., Bendszus, M., Schlemmer, H. Peter, and Radbruch, A., Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study, Investigative Radiology, vol. 54, pp. 653–660, 2019.
J. Kleesiek, Morshuis, J. Nikolas, Isensee, F., Deike-Hofmann, K., Paech, D., Kickingereder, P., Köthe, U., Rother, C., Forsting, M., Wick, W., Bendszus, M., Schlemmer, H. Peter, and Radbruch, A., Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study, Investigative Radiology, vol. 54, pp. 653–660, 2019.
D. Kotovenko, Sanakoyeu, A., Lang, S., and Ommer, B., Content and Style Disentanglement for Artistic Style Transfer, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2019.
M. Haußmann, Hamprecht, F. A., and Kandemir, M., Deep Active Learning with Adaptive Acquisition, IJCAI. Proceedings. pp. 2470-2476, 2019.PDF icon Technical Report (137.6 KB)
L. Kiefer, Storath, M., and Weinmann, A., An efficient algorithm for the piecewise affine-linear Mumford-Shah model based on a Taylor jet splitting, IEEE Transactions on Image Processing, vol. 29, 2019.PDF icon Technical Report (2.04 MB)
A. Imle, Kumberger, P., Schnellbächer, N. D., Fehr, J., Carillo-Bustamente, P., Ales, J., Schmidt, P., Ritter, C., Godinez, W. J., Müller, B., Rohr, K., Hamprecht, F. A., Schwarz, U. S., Graw, F., and Fackler, O. T., Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures, Nature Communications, vol. 13;10(1), 2019.
D. M. Kirchhöfer, Holst, G. A., Wouters, F. S., Hock, S., and Jähne, B., Extended noise equalisation for image compression in microscopical applications, tm - Technisches Messen, vol. 86, pp. 422–432, 2019.
A. Klein, The Fetch Dependency of Small-Scale Air-Sea Interaction Processes at Low to Moderate Wind Speeds, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, Heidelberg, 2019.
L. Kostrykin, Schnörr, C., and Rohr, K., Globally Optimal Segmentation of Cell Nuclei in Fluoroscence Microscopy Images using Shape and Intensity Information, Medical Image Analysis, 2019.
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., Guided Image Generation with Conditional Invertible Neural Networks, 2019.
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., Guided Image Generation with Conditional Invertible Neural Networks, 2019.
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., Guided Image Generation with Conditional Invertible Neural Networks, 2019.
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., Guided Image Generation with Conditional Invertible Neural Networks, 2019.
S. Berg, Kutra, D., Kroeger, T., Straehle, C. N., Kausler, B. X., Haubold, C., Schiegg, M., Ales, J., Beier, T., Rudy, M., Eren, K., Cervantes, J. I., Xu, B., Beuttenmüller, F., Wolny, A., Zhang, C., Köthe, U., Hamprecht, F. A., and Kreshuk, A., ilastik: interactive machine learning for (bio)image analysis, Nature Methods, vol. 16, pp. 1226-1232, 2019.
S. Berg, Kutra, D., Kroeger, T., Straehle, C. N., Kausler, B. X., Haubold, C., Schiegg, M., Ales, J., Beier, T., Rudy, M., Eren, K., Cervantes, J. I., Xu, B., Beuttenmüller, F., Wolny, A., Zhang, C., Köthe, U., Hamprecht, F. A., and Kreshuk, A., ilastik: interactive machine learning for (bio)image analysis, Nature Methods, vol. 16, pp. 1226-1232, 2019.
S. Berg, Kutra, D., Kroeger, T., Straehle, C. N., Kausler, B. X., Haubold, C., Schiegg, M., Ales, J., Beier, T., Rudy, M., Eren, K., Cervantes, J. I., Xu, B., Beuttenmüller, F., Wolny, A., Zhang, C., Köthe, U., Hamprecht, F. A., and Kreshuk, A., ilastik: interactive machine learning for (bio)image analysis, Nature Methods, vol. 16, pp. 1226-1232, 2019.
S. Berg, Kutra, D., Kroeger, T., Straehle, C. N., Kausler, B. X., Haubold, C., Schiegg, M., Ales, J., Beier, T., Rudy, M., Eren, K., Cervantes, J. I., Xu, B., Beuttenmüller, F., Wolny, A., Zhang, C., Köthe, U., Hamprecht, F. A., and Kreshuk, A., ilastik: interactive machine learning for (bio)image analysis, Nature Methods, vol. 16, pp. 1226-1232, 2019.
S. Berg, Kutra, D., Kroeger, T., Straehle, C. N., Kausler, B. X., Haubold, C., Schiegg, M., Ales, J., Beier, T., Rudy, M., Eren, K., Cervantes, J. I., Xu, B., Beuttenmüller, F., Wolny, A., Zhang, C., Köthe, U., Hamprecht, F. A., and Kreshuk, A., ilastik: interactive machine learning for (bio)image analysis, Nature Methods, vol. 16, pp. 1226-1232, 2019.
E. Kirschbaum, Haußmann, M., Wolf, S., Sonntag, H., Schneider, J., Elzoheiry, S., Kann, O., Durstewitz, D., and Hamprecht, F. A., LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos, ICLR. Proceedings. 2019.
E. Kirschbaum, Haußmann, M., Wolf, S., Sonntag, H., Schneider, J., Elzoheiry, S., Kann, O., Durstewitz, D., and Hamprecht, F. A., LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos, ICLR. Proceedings. 2019.
L. Nagel, Krall, K. E., and Jähne, B., Measurement of air-sea gas transfer velocities in the Baltic Sea, Ocean Science, vol. 15, pp. 235–247, 2019.
Y. Bengio, Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A., and Pal, C., A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms, arXiv preprint arXiv:1901.10912, 2019.PDF icon Technical Report (871.59 KB)
E. Kirschbaum, Novel Machine Learning Approaches for Neurophysiological Data Analysis. Heidelberg University, 2019.
T. J. Adler, Ayala, L., Ardizzone, L., Kenngott, H. G., Vemuri, A., Müller-Stich, B. P., Rother, C., Köthe, U., and Maier-Hein, L., Out of Distribution Detection for Intra-operative Functional Imaging, in MICCAI UNSURE Workshop 2019, 2019, vol. 11840 LNCS, pp. 75–82.PDF icon PDF (3.1 MB)
T. J. Adler, Ayala, L., Ardizzone, L., Kenngott, H. G., Vemuri, A., Müller-Stich, B. P., Rother, C., Köthe, U., and Maier-Hein, L., Out of Distribution Detection for Intra-operative Functional Imaging, in MICCAI UNSURE Workshop 2019, 2019, vol. 11840 LNCS, pp. 75–82.PDF icon PDF (3.1 MB)
M. Haußmann, Hamprecht, F. A., and Kandemir, M., Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation, UAI. Proceedings. pp. 563-573, 2019.PDF icon Technical Report (1.04 MB)
M. Storath, Kiefer, L., and Weinmann, A., Smoothing for signals with discontinuities using higher order Mumford-Shah models, Numerische Mathematik, vol. 143(2), pp. 423-460, 2019.PDF icon Technical Report (1.09 MB)
D. Kotovenko, Sanakoyeu, A., Lang, S., Ma, P., and Ommer, B., Using a Transformation Content Block For Image Style Transfer, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.
2018
T. Hodaň, Michel, F., Brachmann, E., Kehl, W., Buch, A. Glent, Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T. Kyun, Matas, J., and Rother, C., BOP: Benchmark for 6D object pose estimation, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11214 LNCS, pp. 19–35.
T. Hodaň, Michel, F., Brachmann, E., Kehl, W., Buch, A. Glent, Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T. Kyun, Matas, J., and Rother, C., BOP: Benchmark for 6D object pose estimation, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11214 LNCS, pp. 19–35.
T. Hodaň, Michel, F., Brachmann, E., Kehl, W., Buch, A. Glent, Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T. Kyun, Matas, J., and Rother, C., BOP: Benchmark for 6D object pose estimation, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11214 LNCS, pp. 19–35.
A. Arnab, Zheng, S., Jayasumana, S., Romera-paredes, B., Kirillov, A., Savchynskyy, B., Rother, C., Kahl, F., and Torr, P., Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation, Cvpr, vol. XX, pp. 1–15, 2018.
A. Arnab, Zheng, S., Jayasumana, S., Romera-paredes, B., Kirillov, A., Savchynskyy, B., Rother, C., Kahl, F., and Torr, P., Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation, Cvpr, vol. XX, pp. 1–15, 2018.
A. - S. Wahl, Erlebach, E., Brattoli, B., Büchler, U., Kaiser, J., Ineichen, V. B., Mosberger, A. C., Schneeberger, S., Imobersteg, S., Wieckhorst, M., Stirn, M., Schroeter, A., Ommer, B., and Schwab, M. E., Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats, Sage Journals, vol. Journal of Cerebral Blood Flow & Metabolism, 2018.PDF icon 0271678x18777661.pdf (770.87 KB)
J. Kunz and Jähne, B., Investigating small scale air-sea exchange processes via thermography, Front. Mech. Eng., vol. 26, 2018.

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