30 citations · 32 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
Leonid Mill, Oliver Aust, Jochen A. Ackermann +12
Artificial intelligence (AI), machine learning, and deep learning (DL) methods are becoming increasingly important in the field of biomedical image analysis. However, to exploit th…
Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography
Fabian Wagner, Mareike Thies, Mingxuan Gu +9
Computed tomography is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution and radiation dose…