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Hanna Siebert
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2020 – today
- 2023
- [j5]Mattias P. Heinrich
, Hanna Siebert
Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation. Sensors 23(6): 2876 (2023) - [j4]Kumar T. Rajamani
Attention-augmented U-Net (AA-U-Net) for semantic segmentation. Signal Image Video Process. 17(4): 981-989 (2023) - [j3]Alessa Hering
Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning. IEEE Trans. Medical Imaging 42(3): 697-712 (2023) - [c9]Laura F. Graf, Hanna Siebert, Sven Mischkewitz, Ron Keuth, Mattias P. Heinrich:
Highly accurate deep registration networks for large deformation estimation in compression ultrasound. Medical Imaging: Image Processing 2023 - 2022
- [j2]Hanna Siebert
Learning a Metric for Multimodal Medical Image Registration without Supervision Based on Cycle Constraints. Sensors 22(3): 1107 (2022) - [c8]Christoph Großbröhmer
Employing ConvexAdam for BraTS-Reg. BrainLes@MICCAI 2022: 252-261 - [c7]Hanna Siebert
Learn to Fuse Input Features for Large-Deformation Registration with Differentiable Convex-Discrete Optimisation. WBIR 2022: 119-123 - [c6]Mona Schumacher, Hanna Siebert, Ragnar Bade, Andreas Genz, Mattias P. Heinrich:
Weak Bounding Box Supervision for Image Registration Networks. WBIR 2022: 215-219 - [i3]Hanna Siebert, Marian Himstedt, Mattias P. Heinrich:
Learn2Trust: A video and streamlit-based educational programme for AI-based medical image analysis targeted towards medical students. CoRR abs/2208.07314 (2022) - 2021
- [j1]Kumar T. Rajamani, Hanna Siebert, Mattias P. Heinrich
Dynamic deformable attention network (DDANet) for COVID-19 lesions semantic segmentation. J. Biomed. Informatics 119: 103816 (2021) - [c5]Hanna Siebert, Lasse Hansen, Mattias P. Heinrich
Evaluating Design Choices for Deep Learning Registration Networks - Architecture Matters. Bildverarbeitung für die Medizin 2021: 111-116 - [c4]Hanna Siebert
Fast 3D Registration with Accurate Optimisation and Little Learning for Learn2Reg 2021. MIDOG/MOOD/Learn2Reg@MICCAI 2021: 174-179 - [c3]Prateek Gupta, Hanna Siebert, Mattias P. Heinrich
DA-AR-Net: an attentive activation based Deformable auto-encoder for group-wise registration. Medical Imaging: Image Processing 2021 - [c2]Hanna Siebert, Kumar T. Rajamani, Mattias P. Heinrich
Learning inverse consistent 3D groupwise registration with deforming autoencoders. Medical Imaging: Image Processing 2021 - [i2]Hanna Siebert, Lasse Hansen, Mattias P. Heinrich:
Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021. CoRR abs/2112.03053 (2021) - [i1]Alessa Hering, Lasse Hansen, Tony C. W. Mok, Albert C. S. Chung, Hanna Siebert, Stephanie Häger, Annkristin Lange, Sven Kuckertz, Stefan Heldmann, Wei Shao, Sulaiman Vesal, Mirabela Rusu, Geoffrey A. Sonn, Théo Estienne, Maria Vakalopoulou, Luyi Han, Yunzhi Huang, Mikael Brudfors, Yaël Balbastre, Samuel Joutard, Marc Modat, Gal Lifshitz, Dan Raviv, Jinxin Lv, Qiang Li, Vincent Jaouen, Dimitris Visvikis, Constance Fourcade, Mathieu Rubeaux, Wentao Pan, Zhe Xu, Bailiang Jian, Francesca De Benetti, Marek Wodzinski, Niklas Gunnarsson, Huaqi Qiu, Zeju Li, Christoph Großbröhmer, Andrew Hoopes, Ingerid Reinertsen, Yiming Xiao, Bennett A. Landman, Yuankai Huo, Keelin Murphy, Bram van Ginneken, Adrian V. Dalca, Mattias P. Heinrich:
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. CoRR abs/2112.04489 (2021) - 2020
- [c1]Hanna Siebert, Mattias P. Heinrich
Deep Groupwise Registration of MRI Using Deforming Autoencoders. Bildverarbeitung für die Medizin 2020: 236-241
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