Results 251 to 260 of about 757,511 (305)

A Prospective and Cost-Effective Voltammetric-Chemometric Approach for the Discrimination and Quantification of Melamine and Urea in Milk. [PDF]

ACS Omega
Putra BR, Nikma SH, Rafi M, Wahyuni WT.
europepmc   +1 more source

AI Do's and Don'ts in Cancer Imaging: Remembering the Patient Behind the Pixel. [PDF]

Radiol Imaging Cancer
Zandieh G, Singh Y, DeSilvio T, Flannery B.   +3 more
europepmc   +1 more source

A bird-inspired artificial intelligence framework for advanced large text summarization. [PDF]

Front Artif Intell
Huang B, Bari A.
europepmc   +1 more source

A data fusion deep learning approach for accurate organelle-based classification of cancer cells. [PDF]

Health Inf Sci Syst
Yee H, Bouyea M, Goldwag J, Lamar JM, Intes X, Kruger U, Barroso M.   +6 more
europepmc   +1 more source

Clinically significant prostate cancer detection with deep learning in a multi-center magnetic resonance imaging study. [PDF]

Sci Rep
Alzate-Grisales JA, Mora-Rubio A, Peán-Teruel M, Beltrán AN, Torres CR, García JMO, García-García F, Tabares-Soto R, García-Gómez JM, de la Iglesia-Vayá M.   +9 more
europepmc   +1 more source

High-temporal resolution metabolic connectivity resolved by component-based noise correction. [PDF]

J Cereb Blood Flow Metab
Reed MB, Graf S, Murgaš M, Eggerstorfer B, Milz C, Silberbauer LR, Falb P, Briem E, Mayerweg A, Schlosser G, Klug S, Nics L, Godbersen GM, Rasul S, Hacker M, Hahn A, Lanzenberger R.   +16 more
europepmc   +1 more source

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Preprocessing

2017
In neuroimaging studies, a number of preprocessing steps are often applied to MRI data to correct for artifacts that arise during acquisition. This chapter discusses the main options for arterial spin labeling (ASL) data, along with some of the specific ways in which these can improve the data, but can also interact with subsequent analysis steps.
Michael Chappell, Bradley MacIntosh, Thomas Okell   +2 more
  +4 more sources