1 citations · 1 across the 4 of their papers we have counts for
4 papers
SynthRAR: Ring Artifacts Reduction in CT with Unrolled Network and Synthetic Data Training
Hongxu Yang, Levente Lippenszky, Edina Timko +1
Defective and inconsistent responses in CT detectors can cause ring and streak artifacts in the reconstructed images, making them unusable for clinical purposes. In recent years, s…
Low performing pixel correction in computed tomography with unrolled network and synthetic data training
Hongxu Yang, Levente Lippenszky, Edina Timko +2
Low performance pixels (LPP) in Computed Tomography (CT) detectors would lead to ring and streak artifacts in the reconstructed images, making them clinically unusable. In recent y…
SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model
Hongxu Yang, Edina Timko, Levente Lippenszky +2
Synthetic tumors in medical images offer controllable characteristics that facilitate the training of machine learning models, leading to an improved segmentation performance. Howe…
Zero-shot Bias Correction: Efficient MR Image Inhomogeneity Reduction Without Any Data
Hongxu Yang, Edina Timko, Brice Fernandez
In recent years, deep neural networks for image inhomogeneity reduction have shown promising results. However, current methods with (un)supervised solutions require preparing a tra…