25 citations · 46 across the 4 of their papers we have counts for
4 papers
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation
Hadas Ben-Atya, Ori Rajchert, Liran Goshen +1
Automatic brain tumor segmentation from Magnetic Resonance Imaging (MRI) data plays an important role in assessing tumor response to therapy and personalized treatment stratificati…
Learning a sparse database for patch-based medical image segmentation
Moti Freiman, Hannes Nickisch, Holger Schmitt +4
We introduce a functional for the learning of an optimal database for patch-based image segmentation with application to coronary lumen segmentation from coronary computed tomograp…
Improving CCTA based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation
Moti Freiman, Hannes Nickisch, Sven Prevrhal +5
Purpose: The goal of this study was to assess the potential added benefit of accounting for partial volume effects (PVE) in an automatic coronary lumen segmentation algorithm from…
Unsupervised Abnormality Detection through Mixed Structure Regularization (MSR) in Deep Sparse Autoencoders
Moti Freiman, Ravindra Manjeshwar, Liran Goshen
Deep sparse auto-encoders with mixed structure regularization (MSR) in addition to explicit sparsity regularization term and stochastic corruption of the input data with Gaussian n…