Deep Groupwise Registration of MRI Using Deforming Autoencoders. - Hanna Siebert; Mattias P. Heinrich (Bildverarbeitung für die Medizin, 2020)

Evaluating Design Choices for Deep Learning Registration Networks - Architecture Matters. - Hanna Siebert; Lasse Hansen; Mattias P. Heinrich (Bildverarbeitung für die Medizin, 2021)

Fast 3D Registration with Accurate Optimisation and Little Learning for Learn2Reg 2021. - Hanna Siebert; Lasse Hansen; Mattias P. Heinrich (MIDOG/MOOD/Learn2Reg@MICCAI, 2021)

DA-AR-Net: an attentive activation based Deformable auto-encoder for group-wise registration. - Prateek Gupta; Hanna Siebert; Mattias P. Heinrich; Kumar T. Rajamani (Image Processing, 2021)

Learning inverse consistent 3D groupwise registration with deforming autoencoders. - Hanna Siebert; Kumar T. Rajamani; Mattias P. Heinrich (Image Processing, 2021)

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. - Alessa Hering; Lasse Hansen; Tony C. W. Mok; Albert C. S. Chung; Hanna Siebert; Stephanie Häger; Annkristin Lange; Sven Kuckertz; Stefan Heldmann; Wei Shao 008; Sulaiman Vesal; Mirabela Rusu; et al. (CoRR, 2021)

Dynamic deformable attention network (DDANet) for COVID-19 lesions semantic segmentation. - Kumar T. Rajamani; Hanna Siebert; Mattias P. Heinrich (J. Biomed. Informatics, 2021)

Employing ConvexAdam for BraTS-Reg. - Christoph Großbröhmer; Hanna Siebert; Lasse Hansen; Mattias P. Heinrich (BrainLes@MICCAI, 2022)

Weak Bounding Box Supervision for Image Registration Networks. - Mona Schumacher; Hanna Siebert; Ragnar Bade; Andreas Genz; Mattias P. Heinrich (WBIR, 2022)

Learn to Fuse Input Features for Large-Deformation Registration with Differentiable Convex-Discrete Optimisation. - Hanna Siebert; Mattias P. Heinrich (WBIR, 2022)

Learn2Trust: A video and streamlit-based educational programme for AI-based medical image analysis targeted towards medical students. - Hanna Siebert; Marian Himstedt; Mattias P. Heinrich (CoRR, 2022)

Learning a Metric for Multimodal Medical Image Registration without Supervision Based on Cycle Constraints. - Hanna Siebert; Lasse Hansen; Mattias P. Heinrich (Sensors, 2022)

Highly accurate deep registration networks for large deformation estimation in compression ultrasound. - Laura F. Graf; Hanna Siebert; Sven Mischkewitz; Ron Keuth; Mattias P. Heinrich (Image Processing, 2023)

Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation. - Mattias P. Heinrich; Hanna Siebert; Laura Graf 001; Sven Mischkewitz; Lasse Hansen (Sensors, 2023)

Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning. - Alessa Hering; Lasse Hansen; Tony C. W. Mok; Albert C. S. Chung; Hanna Siebert; Stephanie Häger; Annkristin Lange; Sven Kuckertz; Stefan Heldmann; Wei Shao 008; Sulaiman Vesal; Mirabela Rusu; et al. (IEEE Trans. Medical Imaging, 2023)

Attention-augmented U-Net (AA-U-Net) for semantic segmentation. - Kumar T. Rajamani; Priya Rani; Hanna Siebert; Elagiri Ramalingam Rajkumar; Mattias P. Heinrich (Signal Image Video Process., 2023)

Abstract: ConvexAdam - Self-configuring Framework for Dual-Optimisation-based 3D Multitask Medical Image Registration. - Christoph Großbröhmer; Hanna Siebert; Lasse Hansen; Mattias P. Heinrich (Bildverarbeitung für die Medizin, 2025)

ConvexAdam: Self-Configuring Dual-Optimization-Based 3D Multitask Medical Image Registration. - Hanna Siebert; Christoph Großbröhmer; Lasse Hansen; Mattias P. Heinrich (IEEE Trans. Medical Imaging, 2025)