Latent Gaussian random field mixture models

D Bolin, J Wallin, F Lindgren - Computational Statistics & Data Analysis, 2019 - Elsevier
For many problems in geostatistics, land cover classification, and brain imaging the classical
Gaussian process models are unsuitable due to sudden, discontinuous, changes in the …

Statistical learning in computed tomography image estimation

FL Bayisa, X Liu, A Garpebring, J Yu - Medical physics, 2018 - Wiley Online Library
Purpose There is increasing interest in computed tomography (CT) image estimations from
magnetic resonance (MR) images. The estimated CT images can be utilized for attenuation …

Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images

K Kuljus, FL Bayisa, D Bolin, J Lember… - … in Statistics: Case …, 2018 - Taylor & Francis
Two principal areas of application for estimated computed tomography (CT) images are
dose calculations in magnetic resonance imaging (MRI) based radiotherapy treatment …

Deep neural network with FGL for small dataset classification

C Guo, R Li, M Yang, X Tang - IET Image Processing, 2019 - Wiley Online Library
In certain applications, classification models have to be trained with small datasets. This
study proposes a new deep neural network with a feature generalisation layer (FGL). First …

Statistical Methods in Computed Tomography Image Estimation

FL Bayisa, X Liu, A Garpebring, J Yu - arXiv preprint arXiv:1805.11126, 2018 - arxiv.org
Purpose: There is increasing interest in computed tomography (CT) image estimations from
magnetic resonance (MR) images. The estimated CT images can be utilised for attenuation …

Computed Tomography Image Estimation by Statistical Learning Methods

FL Bayisa, X Liu, A Garpebring, J Yu - 2018 - diva-portal.org
There is increasing interest in computed tomography (CT) image estimations from magnetic
resonance (MR) images. The estimated CT images can be utilised for attenuation correction …