Lena Maier-Hein
German Cancer Research Center
334 Papers
1.1K Citations
Lena Maier-Hein is an academic researcher from German Cancer Research Center. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 36, co-authored 273 publications. Previous affiliations of Lena Maier-Hein include Heidelberg University & Karlsruhe Institute of Technology.
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Papers
Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging
Jan-Hinrich Nölke,Jan-Hinrich Nölke,Tim Adler,Tim Adler,Janek Gröhl,Thomas Kirchner,Lynton Ardizzone,Carsten Rother,Ullrich Köthe,Lena Maier-Hein,Lena Maier-Hein +10 more
- 01 Jan 2021
TL;DR: In this article, a conditional invertible neural network (CINN) was proposed to handle the inverse reconstruction problem in multispectral photoacoustic imaging (PAI).
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Iterative closest point algorithm with anisotropic weighting and its application to fine surface registration
TL;DR: The first variant of the ICP that accounts for anisotropic localization uncertainty in both shapes as well as in both steps of the algorithm is presented and how to apply the method for robust fine registration of surface meshes is shown.
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Minimally deformed correspondences between surfaces for intra-operative registration
Thiago R. Dos Santos,Caspar J. Goch,Alfred M. Franz,Hans-Peter Meinzer,Tobias Heimann,Lena Maier-Hein +5 more
TL;DR: This work presents a method for automatically finding correspondences between surfaces that searches for minimally deformed configurations and shows to be reliable for the registration of partial multi-modal surfaces with noise and distortions.
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Uncertainty handling in intra-operative multispectral imaging with invertible neural networks
Tim Adler,Lynton Ardizzone,Leonardo Ayala,Janek Gröhl,Anant Vemuri,Sebastian J. Wirkert,Beat P. Müller-Stich,Carsten Rother,Ullrich Köthe,Lena Maier-Hein +9 more
- 17 Apr 2019
TL;DR: This research presents a novel and scalable approach to Minimally-invasive Surgery of the Department of General Surgery with real-world implications for the treatment of cancer and its Kessler’s disease.
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Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks.
Darya Trofimova,Tim Adler,Lisa Kausch,Lynton Ardizzone,Klaus H. Maier-Hein,Ullrich Köthe,Carsten Rother,Lena Maier-Hein +7 more
TL;DR: The hypothesis of this work is that the proposed approach is able to identify multiple solutions in such ambiguous registration problems by representing the possible solutions to a registration problem by a probability distribution that encodes different plausible solutions via multiple modes.
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