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 | 7569 | 2016 |
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 | 1715 | 2018 |
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 | 670 | 2019 |
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 | 243 | 2018 |
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 | 204 | 2019 |
Parting with Illusions about Deep Active Learning S Mittal, M Tatarchenko, Ö Cicek, T Brox arXiv Preprint, https://arxiv.org/abs/1912.05361, 2019 | 51 | 2019 |
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 | 40 | 2020 |
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 | 39 | 2021 |
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 | 18 | 2021 |
Microridge-like structures anchor motile cilia T Yasunaga, J Wiegel, MD Bergen, M Helmstädter, D Epting, A Paolini, ... Nature Communications 13 (1), 2056, 2022 | 17 | 2022 |
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 | 7 | 2019 |
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 | 5 | 2020 |
Requirements for mammalian promoters to decode transcription factor dynamics EB Antwi, Y Marrakchi, Ö Çiçek, T Brox, B Di Ventura Nucleic Acids Research 51 (9), 4674-4690, 2023 | 3 | 2023 |
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. Medical Image Computing and Computer-Assisted Intervention (MICCAI) O Cicek, A Abdulkadir, S Lienkamp, T Brox, O Ronneberger Lecture Notes in Computer Science, 424-432, 2015 | 3 | 2015 |
Mixture distribution estimation for future prediction T Brox, O Makansi, Ö Cicek, ILG Eddy US Patent 12,014,270, 2024 | 2 | 2024 |
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 |
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 |