Titlebook: Machine Learning for Medical Image Reconstruction; First International Florian Knoll,Andreas Maier,Daniel Rueckert Conference proceedings

anachronistic

Notorious

Left Atria Reconstruction from a Series of Sparse Catheter Paths Using Neural Networksthe given paths. We compare the results against training from partial data generated by the intersection of a randomly generated sphere and the atria. We test the presented network on actual lab phantoms and show promising results.

清醒

簡(jiǎn)略

Bayesian Deep Learning for Accelerated MR Image Reconstruction, we qualitatively show that there seems to be a correlation between the magnitude of the produced uncertainty maps and the error maps, demonstrating the potential utility of the Bayesian DL methods for assessing the reliability of the reconstructed images.

痛恨

Comedienne

Towards Arbitrary Noise Augmentation—Deep Learning for Sampling from Arbitrary Probability Distributling methods such as inversion sampling, rejection sampling, Gaussian mixture models and Markov-Chain-Monte-Carlo. Our model has high sampling efficiency and is easily applied to any probability distribution, without the need of further analytical or numerical calculations.

CHARM

懦夫

MELON

0302-9743 21 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography, and deep learning for general image reconstruction..978-3-030-00128-5978-3-030-00129-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

Urea508