0000000000517499
AUTHOR
Lukáš Pospíšil
Low-cost scalable discretization, prediction and feature selection for complex systems
The introduced data-driven tool allows simultaneous feature selection, model inference, and marked cost and quality gains.
Quality-preserving low-cost probabilistic 3D denoising with applications to Computed Tomography
AbstractWe propose a pipeline for a synthetic generation of personalized Computer Tomography (CT) images, with a radiation exposure evaluation and a lifetime attributable risk (LAR) assessment. We perform a patient-specific performance evaluation for a broad range of denoising algorithms (including the most popular Deep Learning denoising approaches, wavelets-based methods, methods based on Mumford-Shah denoising etc.), focusing both on accessing the capability to reduce the patient-specific CT-induced LAR and on computational cost scalability. We introduce a parallel probabilistic Mumford-Shah denoising model (PMS), showing that it markedly-outperforms the compared common denoising methods…