138 citations · 370 across the 21 of their papers we have counts for
8 papers · 2 filters
Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks
Anton S. Becker, Lukas Jendele, Ondrej Skopek +4
To train a cycle-consistent generative adversarial network (CycleGAN) on mammographic data to inject or remove features of malignancy, and to determine whether t…
Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
Katarína Tóthová, Sarah Parisot, Matthew C. H. Lee +5
Surface reconstruction is a vital tool in a wide range of areas of medical image analysis and clinical research. Despite the fact that many methods have proposed solutions to the r…
Combining Heterogeneously Labeled Datasets For Training Segmentation Networks
Jana Kemnitz, Christian F. Baumgartner, Wolfgang Wirth +3
Accurate segmentation of medical images is an important step towards analyzing and tracking disease related morphological alterations in the anatomy. Convolutional neural networks…
Iterative Interaction Training for Segmentation Editing Networks
Gustav Bredell, Christine Tanner, Ender Konukoglu
Automatic segmentation has great potential to facilitate morphological measurements while simultaneously increasing efficiency. Nevertheless often users want to edit the segmentati…
Generative Adversarial Networks for MR-CT Deformable Image Registration
Christine Tanner, Firat Ozdemir, Romy Profanter +3
Deformable Image Registration (DIR) of MR and CT images is one of the most challenging registration task, due to the inherent structural differences of the modalities and the missi…
Learning to Segment Medical Images with Scribble-Supervision Alone
Yigit B. Can, Krishna Chaitanya, Basil Mustafa +3
Semantic segmentation of medical images is a crucial step for the quantification of healthy anatomy and diseases alike. The majority of the current state-of-the-art segmentation al…