1 citations · 1 across the 3 of their papers we have counts for
3 papers
Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising
Fabian Wagner, Mareike Thies, Laura Pfaff +10
Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usual…
Metal-conscious Embedding for CBCT Projection Inpainting
Fuxin Fan, Yangkong Wang, Ludwig Ritschl +6
The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. I…
An unobtrusive quality supervision approach for medical image annotation
Sonja Kunzmann, Mathias Öttl, Prathmesh Madhu +2
Image annotation is one essential prior step to enable data-driven algorithms. In medical imaging, having large and reliably annotated data sets is crucial to recognize various dis…