Genome expansion and gene loss in powdery mildew fungi reveal tradeoffs in extreme parasitism PD Spanu, JC Abbott, J Amselem, TA Burgis, DM Soanes, K Stüber, ... Science 330 (6010), 1543-1546, 2010 | 886 | 2010 |
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker JH Cole, RPK Poudel, D Tsagkrasoulis, MWA Caan, C Steves, ... NeuroImage 163, 115-124, 2017 | 792 | 2017 |
Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks A Payan, G Montana arXiv preprint arXiv:1502.02506, 2015 | 645 | 2015 |
Deep neural networks for anatomical brain segmentation A de Brebisson, G Montana Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 436 | 2015 |
Brown and white adipose tissues: intrinsic differences in gene expression and response to cold exposure in mice M Rosell, M Kaforou, A Frontini, A Okolo, YW Chan, E Nikolopoulou, ... American Journal of Physiology-Endocrinology and Metabolism 306 (8), E945-E964, 2014 | 407 | 2014 |
Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation RPK Poudel, P Lamata, G Montana Reconstruction, Segmentation, and Analysis of Medical Images: First …, 2017 | 332 | 2017 |
Automated triaging of adult chest radiographs with deep artificial neural networks M Annarumma, SJ Withey, RJ Bakewell, E Pesce, V Goh, G Montana Radiology 291 (1), 196-202, 2019 | 274 | 2019 |
Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach M Vounou, TE Nichols, G Montana, ... Neuroimage 53 (3), 1147-1159, 2010 | 254 | 2010 |
Smchd1-dependent and-independent pathways determine developmental dynamics of CpG island methylation on the inactive X chromosome AV Gendrel, A Apedaile, H Coker, A Termanis, I Zvetkova, J Godwin, ... Developmental cell 23 (2), 265-279, 2012 | 222 | 2012 |
Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks PP Ypsilantis, M Siddique, HM Sohn, A Davies, G Cook, V Goh, ... PloS one 10 (9), e0137036, 2015 | 200 | 2015 |
Estimating time-varying brain connectivity networks from functional MRI time series RP Monti, P Hellyer, D Sharp, R Leech, C Anagnostopoulos, G Montana NeuroImage 103, 427-443, 2014 | 197 | 2014 |
Statistical tests for admixture mapping with case-control and cases-only data G Montana, JK Pritchard The American Journal of Human Genetics 75 (5), 771-789, 2004 | 182 | 2004 |
Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease M Vounou, E Janoušová, R Wolz, JL Stein, PM Thompson, D Rueckert, ... Elsevier, The Nederlands. ISSN, 2012 | 177 | 2012 |
False positives in neuroimaging genetics using cluster-size inference M Silver, G Montana, TE Nichols Neuroimage 54 (2), 992-1000, 2010 | 150* | 2010 |
Learning to detect chest radiographs containing pulmonary lesions using visual attention networks E Pesce, SJ Withey, PP Ypsilantis, R Bakewell, V Goh, G Montana Medical image analysis 53, 26-38, 2019 | 134 | 2019 |
Whole-brain mapping of structural connectivity in infants reveals altered connection strength associated with growth and preterm birth AS Pandit, E Robinson, P Aljabar, G Ball, IS Gousias, Z Wang, JV Hajnal, ... Cerebral cortex 24 (9), 2324-2333, 2014 | 114 | 2014 |
Community detection in multiplex networks using locally adaptive random walks Z Kuncheva, G Montana Proceedings of the 2015 IEEE/ACM International Conference on Advances in …, 2015 | 106 | 2015 |
Modelling radiological language with bidirectional long short-term memory networks S Cornegruta, R Bakewell, S Withey, G Montana arXiv preprint arXiv:1609.08409, 2016 | 104 | 2016 |
Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression M Silver, E Janousova, X Hua, PM Thompson, G Montana, ... NeuroImage 63 (3), 1681-1694, 2012 | 102 | 2012 |
The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI R Lorenz, RP Monti, IR Violante, C Anagnostopoulos, AA Faisal, ... NeuroImage 129, 320-334, 2016 | 92 | 2016 |