A Diffeomorphic Framework for Surrogate-based Motion Estimation in Radiation Therapy: Concept and First Evaluation. - René Werner; Jan Ehrhardt; Alexander Schmidt-Richberg; Matthias Wilms; Maximilian Blendowski; Heinz Handels (GI-Jahrestagung, 2012)

Simulation und Evaluation tiefenbildgebender Verfahren zur Prädiktion atmungsbedingter Organ- und Tumorbewegungen. - Maximilian Blendowski; Matthias Wilms; René Werner; Heinz Handels (Bildverarbeitung für die Medizin, 2013)

Surrogate-based diffeomorphic motion estimation for radiation therapy: comparison of multivariate regression approaches. - Matthias Wilms; René Werner; Jan Ehrhardt; Alexander Schmidt-Richberg; Maximilian Blendowski; Heinz Handels (Image Processing, 2013)

Multi-organ Segmentation Using Vantage Point Forests and Binary Context Features. - Mattias P. Heinrich; Maximilian Blendowski (MICCAI (2), 2016)

Abstract: Kombination binärer Kontextfeatures mit Vantage Point Forests zur Multi-Organ-Segmentierung. - Maximilian Blendowski; Mattias P. Heinrich (Bildverarbeitung für die Medizin, 2017)

3D-CNNs for Deep Binary Descriptor Learning in Medical Volume Data. - Max Blendowski; Mattias P. Heinrich (Bildverarbeitung für die Medizin, 2018)

TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions. - Mattias P. Heinrich; Max Blendowski; Ozan Oktay (Int. J. Comput. Assist. Radiol. Surg., 2018)

How to Learn from Unlabeled Volume Data: Self-supervised 3D Context Feature Learning. - Maximilian Blendowski; Hannes Nickisch; Mattias P. Heinrich (MICCAI (6), 2019)

Learning interpretable multi-modal features for alignment with supervised iterative descent. - Max Blendowski; Mattias P. Heinrich (MIDL, 2019)

Combining MRF-based deformable registration and deep binary 3D-CNN descriptors for large lung motion estimation in COPD patients. - Max Blendowski; Mattias P. Heinrich (Int. J. Comput. Assist. Radiol. Surg., 2019)

Abstract: Self-Supervised 3D Context Feature Learning on Unlabeled Volume Data. - Maximilian Blendowski; Mattias P. Heinrich (Bildverarbeitung für die Medizin, 2020)

Abstract: Defence of Mathematical Models for Deep Learning based Registration. - Lasse Hansen; Maximilian Blendowski; Mattias P. Heinrich (Bildverarbeitung für die Medizin, 2020)

Multimodal 3D medical image registration guided by shape encoder-decoder networks. - Max Blendowski; Nassim Bouteldja; Mattias P. Heinrich (Int. J. Comput. Assist. Radiol. Surg., 2020)

Learning to map between ferns with differentiable binary embedding networks. - Max Blendowski; Mattias P. Heinrich (CoRR, 2020)

Weakly-supervised learning of multi-modal features for regularised iterative descent in 3D image registration. - Max Blendowski; Lasse Hansen; Mattias P. Heinrich (Medical Image Anal., 2021)