output
20152020
most citedMachine learning and glioma imaging biomarkers

102 citations

7 papers

eess.IV2020

Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening

Jeremy Tan, Anselm Au, Qingjie Meng +7

Prenatal screening with ultrasound can lower neonatal mortality significantly for selected cardiac abnormalities. However, the need for human expertise, coupled with the high volum…

q-bio.QM20191 cited

An Update on Machine Learning in Neuro-oncology Diagnostics

Thomas Booth

Imaging biomarkers in neuro-oncology are used for diagnosis, prognosis and treatment response monitoring. Magnetic resonance imaging is typically used throughout the patient pathwa…

q-bio.QM2019102 cited

Machine learning and glioma imaging biomarkers

Thomas Booth, Matthew Williams, Aysha Luis +3

Aim: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. Materials an…

eess.IV20194 cited

k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations

Chen Qin, Jo Schlemper, Jinming Duan +4

Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k-space and time. In order to accelerate the dynamic MR imaging and to exploit k-t correlations from highly u…

stat.ME20182 cited

Inequality Constrained Multilevel Models

Bernet S. Kato, Carel F. W. Peeters

Multilevel or hierarchical data structures can occur in many areas of research, including economics, psychology, sociology, agriculture, medicine, and public health. Over the last…

stat.ML201510 cited

Sparse multi-view matrix factorisation: a multivariate approach to multiple tissue comparisons

Zi Wang, Wei Yuan, Giovanni Montana

Gene expression levels in a population vary extensively across tissues. Such heterogeneity is caused by genetic variability and environmental factors, and is expected to be linked…