309 citations · 309 across the 3 of their papers we have counts for
8 papers
Multi-Task Edge Prediction in Temporally-Dynamic Video Graphs
Osman Ülger, Julian Wiederer, Mohsen Ghafoorian +2
Graph neural networks have shown to learn effective node representations, enabling node-, link-, and graph-level inference. Conventional graph networks assume static relations betw…
Find it if You Can: End-to-End Adversarial Erasing for Weakly-Supervised Semantic Segmentation
Erik Stammes, Tom F. H. Runia, Michael Hofmann +1
Semantic segmentation is a task that traditionally requires a large dataset of pixel-level ground truth labels, which is time-consuming and expensive to obtain. Recent advancements…
3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Artificial Deformations
Hessam Sokooti, Bob de Vos, Floris Berendsen +5
We propose a supervised nonrigid image registration method, trained using artificial displacement vector fields (DVF), for which we propose and compare three network architectures.…
I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation
Laurens Samson, Nanne van Noord, Olaf Booij +3
Adversarial training has been recently employed for realizing structured semantic segmentation, in which the aim is to preserve higher-level scene structural consistencies in dense…
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser +41
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are o…
Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT
Hans Meine, Grzegorz Chlebus, Mohsen Ghafoorian +2
Various approaches for liver segmentation in CT have been proposed: Besides statistical shape models, which played a major role in this research area, novel approaches on the basis…