Publications by authors named "Xiang-Wei Ge"
Article Synopsis
- - The study analyzed 263 soil samples from seven land use types around Laizhou Bay to assess heavy metal pollution levels (specifically As, Cr, Ni, Cu, Zn, Cd, Pb, Hg) and found that the surface soil is generally weakly alkaline, with slight exceedances found for As, Ni, Zn, and Hg compared to background values from Weifang.
- - Pollution assessment methods indicated that while the area is generally pollution-free according to the geo-accumulation index, there is light pollution according to the Nemerow comprehensive pollution index, with varying pollution levels across different land types.
- - Source analysis identified that heavy metal pollution primarily stems from natural, transportation, agricultural, and industrial sources, with
Background: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy.
Methods: Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study.
Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning.
Front Neurosci
November 2018
Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas.
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