750 citations · 1.4k across the 30 of their papers we have counts for
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Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data
Thomas Germer, Jan Robine, Sebastian Konietzny +2
Computed tomography (CT) has become an essential part of modern science and medicine. A CT scanner consists of an X-ray source that is spun around an object of interest. On the opp…
A Closer Look at Reference Learning for Fourier Phase Retrieval
Tobias Uelwer, Nick Rucks, Stefan Harmeling
Reconstructing images from their Fourier magnitude measurements is a problem that often arises in different research areas. This process is also referred to as phase retrieval. In…
Non-Iterative Phase Retrieval With Cascaded Neural Networks
Tobias Uelwer, Tobias Hoffmann, Stefan Harmeling
Fourier phase retrieval is the problem of reconstructing a signal given only the magnitude of its Fourier transformation. Optimization-based approaches, like the well-established G…
2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks
Christian Schiffer, Stefan Harmeling, Katrin Amunts +1
Cytoarchitecture describes the spatial organization of neuronal cells in the brain, including their arrangement into layers and columns with respect to cell density, orientation, o…
Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale
Christian Schiffer, Hannah Spitzer, Kai Kiwitz +6
Human brain atlases provide spatial reference systems for data characterizing brain organization at different levels, coming from different brains. Cytoarchitecture is a basic prin…
Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections
Christian Schiffer, Katrin Amunts, Stefan Harmeling +1
Cytoarchitectonic maps provide microstructural reference parcellations of the brain, describing its organization in terms of the spatial arrangement of neuronal cell bodies as meas…
Phase Retrieval Using Conditional Generative Adversarial Networks
Tobias Uelwer, Alexander Oberstraß, Stefan Harmeling
In this paper, we propose the application of conditional generative adversarial networks to solve various phase retrieval problems. We show that including knowledge of the measurem…