8 citations · 22 across the 6 of their papers we have counts for
6 papers
Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
Jianning Li, Jana Fragemann, Seyed-Ahmad Ahmadi +2
The reconstruction loss and the Kullback-Leibler divergence (KLD) loss in a variational autoencoder (VAE) often play antagonistic roles, and tuning the weight of the KLD loss in $β…
The HoloLens in Medicine: A systematic Review and Taxonomy
Christina Gsaxner, Jianning Li, Antonio Pepe +4
The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality display, is the main player in the recent boost in medical augmented reality resea…
Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model
Jianning Li, David G. Ellis, Antonio Pepe +4
Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on…
Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation
Jianning Li, Antonio Pepe, Christina Gsaxner +2
Medical images, especially volumetric images, are of high resolution and often exceed the capacity of standard desktop GPUs. As a result, most deep learning-based medical image ana…
A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge
Jianning Li, Antonio Pepe, Christina Gsaxner +2
In this study, we present a baseline approach for AutoImplant (https://autoimplant.grand-challenge.org/) - the cranial implant design challenge, which, as suggested by the organize…
An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design
Jianning Li, Antonio Pepe, Christina Gsaxner +1
We introduce a fully automatic system for cranial implant design, a common task in cranioplasty operations. The system is currently integrated in Studierfenster (http://studierfens…