Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Jan Egger

Jan Egger

Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder


Sep 29, 2022
Jianning Li, Jana Fragemann, Seyed-Ahmad Ahmadi, Jens Kleesiek, Jan Egger

Figure 1 for Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
Figure 2 for Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
Figure 3 for Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
Figure 4 for Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

The HoloLens in Medicine: A systematic Review and Taxonomy


Sep 06, 2022
Christina Gsaxner, Jianning Li, Antonio Pepe, Yuan Jin, Jens Kleesiek, Dieter Schmalstieg, Jan Egger

Figure 1 for The HoloLens in Medicine: A systematic Review and Taxonomy
Figure 2 for The HoloLens in Medicine: A systematic Review and Taxonomy
Figure 3 for The HoloLens in Medicine: A systematic Review and Taxonomy
Figure 4 for The HoloLens in Medicine: A systematic Review and Taxonomy
* 35 pages, 11 figures 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

GAN-based generation of realistic 3D data: A systematic review and taxonomy


Jul 04, 2022
André Ferreira, Jianning Li, Kelsey L. Pomykala, Jens Kleesiek, Victor Alves, Jan Egger

Figure 1 for GAN-based generation of realistic 3D data: A systematic review and taxonomy
Figure 2 for GAN-based generation of realistic 3D data: A systematic review and taxonomy
Figure 3 for GAN-based generation of realistic 3D data: A systematic review and taxonomy
Figure 4 for GAN-based generation of realistic 3D data: A systematic review and taxonomy
* 52 pages 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

k-strip: A novel segmentation algorithm in k-space for the application of skull stripping


May 19, 2022
Moritz Rempe, Florian Mentzel, Kelsey L. Pomykala, Johannes Haubold, Felix Nensa, Kevin Kröninger, Jan Egger, Jens Kleesiek

Figure 1 for k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
Figure 2 for k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
Figure 3 for k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
Figure 4 for k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
* 11 pages, 6 figures, 2 tables 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model


Apr 12, 2022
Jianning Li, David G. Ellis, Antonio Pepe, Christina Gsaxner, Michele R. Aizenberg, Jens Kleesiek, Jan Egger

Figure 1 for Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model
Figure 2 for Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model
Figure 3 for Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model
Figure 4 for Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model
* 9 pages 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare


Mar 21, 2022
Jana Fragemann, Lynton Ardizzone, Jan Egger, Jens Kleesiek

Figure 1 for Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare
Figure 2 for Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare
Figure 3 for Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare
Figure 4 for Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare
* 43 pages 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate


Feb 21, 2022
Sascha Ranftl, Malte Rolf-Pissarczyk, Gloria Wolkerstorfer, Antonio Pepe, Jan Egger, Wolfgang von der Linden, Gerhard A. Holzapfel

Figure 1 for Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate
Figure 2 for Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate
Figure 3 for Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate
Figure 4 for Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate
* Preprint. 55 pages, 8 figures 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

MOMO -- Deep Learning-driven classification of external DICOM studies for PACS archivation


Dec 01, 2021
Frederic Jonske, Maximilian Dederichs, Moon-Sung Kim, Jan Egger, Lale Umutlu, Michael Forsting, Felix Nensa, Jens Kleesiek

Figure 1 for MOMO -- Deep Learning-driven classification of external DICOM studies for PACS archivation
Figure 2 for MOMO -- Deep Learning-driven classification of external DICOM studies for PACS archivation
Figure 3 for MOMO -- Deep Learning-driven classification of external DICOM studies for PACS archivation
Figure 4 for MOMO -- Deep Learning-driven classification of external DICOM studies for PACS archivation
* 21 pages 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations


Nov 22, 2021
Antonio Pepe, Jan Egger, Marina Codari, Martin J. Willemink, Christina Gsaxner, Jianning Li, Peter M. Roth, Gabriel Mistelbauer, Dieter Schmalstieg, Dominik Fleischmann

Figure 1 for Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations
Figure 2 for Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations
Figure 3 for Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations
Figure 4 for Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations
* 28 pages 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation


Aug 11, 2021
Jianning Li, Antonio Pepe, Christina Gsaxner, Yuan Jin, Jan Egger

Figure 1 for Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation
Figure 2 for Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation
Figure 3 for Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation
Figure 4 for Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation
* 18 pages, 11 figures, 1 table, 14 references 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email
1
2
3
4
5
>>