343 citations
- Philips (Netherlands)NL6 papers
- Technical University of MunichDE5 papers
- KTH Royal Institute of TechnologySE4 papers
- RWTH Aachen UniversityDE4 papers
- TUM KlinikumDE4 papers
- Hamburg University of TechnologyDE3 papers
- Philips (Sweden)SE3 papers
- University Medical Center Hamburg-EppendorfDE3 papers
- University of LübeckDE3 papers
- Eindhoven University of TechnologyNL2 papers
- Fraunhofer Institute for Digital MedicineDE2 papers
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5 papers · 1 filter
Human-sized Magnetic Particle Imaging for Brain Applications
M. Gräser, F. Thieben, P. Szwargulski +11
Determining the brain perfusion is an important task for the diagnosis and treatment of vascular diseases such as occlusions and intracerebral haemorrhage. Even after successful di…
Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease
Danielle F. Pace, Adrian V. Dalca, Tom Brosch +5
We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a…
Deep Learning Based Rib Centerline Extraction and Labeling
Matthias Lenga, Tobias Klinder, Christian Bürger +3
Automated extraction and labeling of rib centerlines is a typically needed prerequisite for more advanced assisted reading tools that help the radiologist to efficiently inspect al…
Towards radiologist-level cancer risk assessment in CT lung screening using deep learning
Stojan Trajanovski, Dimitrios Mavroeidis, Christine Leon Swisher +11
Importance: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and it has been re…
State Space Gaussian Processes with Non-Gaussian Likelihood
Hannes Nickisch, Arno Solin, Alexander Grigorievskiy
We provide a comprehensive overview and tooling for GP modeling with non-Gaussian likelihoods using state space methods. The state space formulation allows for solving one-dimensio…