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Özgün ÇİÇEK
Özgün ÇİÇEK
Department of Computer Science, University of Freiburg, Freiburg, Germany
Bestätigte E-Mail-Adresse bei cs.uni-freiburg.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
3D U-Net: learning dense volumetric segmentation from sparse annotation
Ö Çiçek, A Abdulkadir, SS Lienkamp, T Brox, O Ronneberger
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th …, 2016
50052016
U-Net: deep learning for cell counting, detection, and morphometry
T Falk, D Mai, R Bensch, Ö Cicek, A Abdulkadir, Y Marrakchi, A Böhm, ...
Nature Methods, 2018
11452018
Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation
MJC Jordão, R Sankowski, SM Brendecke, Sagar, G Locatelli, YH Tai, ...
Science 363 (6425), 2019
4812019
Uncertainty estimates and multi-hypotheses networks for optical flow
E Ilg, O Cicek, S Galesso, A Klein, O Makansi, F Hutter, T Brox
Proceedings of the European Conference on Computer Vision (ECCV), 652-667, 2018
1682018
Overcoming limitations of mixture density networks: A sampling and fitting framework for multimodal future prediction
O Makansi, E Ilg, O Cicek, T Brox
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
1412019
Multimodal future localization and emergence prediction for objects in egocentric view with a reachability prior
O Makansi, O Cicek, K Buchicchio, T Brox
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
222020
Parting with Illusions about Deep Active Learning
S Mittal, M Tatarchenko, Ö Cicek, T Brox
arXiv Preprint, https://arxiv.org/abs/1912.05361, 2019
222019
On exposing the challenging long tail in future prediction of traffic actors
O Makansi, Ö Cicek, Y Marrakchi, T Brox
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
112021
Deep learning is widely applicable to phenotyping embryonic development and disease
T Naert, Ö Çiçek, P Ogar, M Bürgi, NI Shaidani, MM Kaminski, Y Xu, ...
Development 148 (21), dev199664, 2021
62021
Learning Representations for Predicting Future Activities
M Zolfaghari, Ö Çiçek, SM Ali, F Mahdisoltani, C Zhang, T Brox
arXiv Preprint, https://arxiv.org/abs/1905.03578, 2019
62019
Recovering the imperfect: Cell segmentation in the presence of dynamically localized proteins
Ö Çiçek, Y Marrakchi, E Boasiako Antwi, B Di Ventura, T Brox
Interpretable and Annotation-Efficient Learning for Medical Image Computing …, 2020
52020
Microridge-like structures anchor motile cilia
T Yasunaga, J Wiegel, MD Bergen, M Helmstädter, D Epting, A Paolini, ...
Nature Communications 13 (1), 2056, 2022
42022
U-Net: deep learning for cell counting, detection, and morphometry (vol 16, pg 67, 2019)
T Falk, D Mai, R Bensch, O Cicek, A Abdulkadir, Y Marrakchi, A Bohm, ...
Nature Methods 16 (4), 351-351, 2019
22019
Mixture distribution estimation for future prediction
T Brox, O Makansi, Ö Cicek, ILG Eddy
US Patent App. 17/616,179, 2022
12022
Search for temporal cell segmentation robustness in phase-contrast microscopy videos
E Gómez-de-Mariscal, H Jayatilaka, Ö Çiçek, T Brox, D Wirtz, ...
arXiv preprint arXiv:2112.08817, 2021
2021
Requirements for mammalian promoters to decode transcription factor dynamics
EB Antwi, Y Marrakchi, Ö Çiçek, T Brox, B Di Ventura
bioRxiv, 2021.10. 12.464037, 2021
2021
Uncertainty Estimation and Its Applications in Computer Vision
Ö Çiçek
https://freidok.uni-freiburg.de/data/194779, 2021
2021
Efficient Computation and Representation of the Diffusion Echo
O Ciçek
Saarland University, 2014
2014
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