| Mutations in genes encoding condensin complex proteins cause microcephaly through decatenation failure at mitosis CA Martin, JE Murray, P Carroll, A Leitch, KJ Mackenzie, M Halachev, ... Genes & development 30 (19), 2158-2172, 2016 | 67 | 2016 |
| Three‐dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours AE Fetit, J Novak, AC Peet, TN Arvanitis NMR in Biomedicine 28 (9), 1174-1184, 2015 | 48 | 2015 |
| Radiomics in paediatric neuro‐oncology: A multicentre study on MRI texture analysis AE Fetit, J Novak, D Rodriguez, DP Auer, CA Clark, RG Grundy, AC Peet, ... NMR in Biomedicine 31 (1), e3781, 2018 | 24 | 2018 |
| 3D texture analysis of MR images to improve classification of paediatric brain tumours: a preliminary study AE Fetit, J Novak, A Peet, T Arvanitis Studies in health technology and informatics 202, 213-6, 2014 | 13* | 2014 |
| #DigitalHealth: Exploring Users' Perspectives through Social Media Analysis S Afyouni, AE Fetit, TN Arvanitis Studies in health technology and informatics 213, 243, 2015 | 7* | 2015 |
| MRI texture analysis in paediatric oncology: a preliminary study. AE Fetit, J Novak, D Rodriguez, DP Auer, CA Clark, RG Grundy, T Jaspan, ... Studies in health technology and informatics 190, 169-171, 2013 | 5* | 2013 |
| A multimodal approach to cardiovascular risk stratification in patients with type 2 diabetes incorporating retinal, genomic and clinical features AE Fetit, AS Doney, S Hogg, R Wang, T MacGillivray, JM Wardlaw, ... Scientific reports 9 (1), 1-10, 2019 | 4 | 2019 |
| 3D Texture Analysis of Heterogeneous MRI Data for Diagnostic Classification of Childhood Brain Tumours. AE Fetit, J Novak, D Rodriguez, DP Auer, CA Clark, RG Grundy, T Jaspan, ... Studies in health technology and informatics 213, 19, 2015 | 3 | 2015 |
| A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling AE Fetit, A Alansary, L Cordero-Grande, J Cupitt, AB Davidson, ... Medical Imaging with Deep Learning, 241-261, 2020 | 2 | 2020 |
| XmoNet: a Fully Convolutional Network for Cross-Modality MR Image Inference S Bano, M Asad, AE Fetit, I Rekik International Workshop on PRedictive Intelligence In MEdicine, 129-137, 2018 | 2 | 2018 |
| Training deep segmentation networks on texture-encoded input: application to neuroimaging of the developing neonatal brain AE Fetit, J Cupitt, T Kart, D Rueckert Medical Imaging with Deep Learning, 230-240, 2020 | 1 | 2020 |
| Reducing Textural Bias Improves Robustness of Deep Segmentation CNNs S Chai, D Rueckert, AE Fetit arXiv preprint arXiv:2011.15093, 2020 | | 2020 |
| Retinal Biomarkers Discovery for Cerebral Small Vessel Disease in an Older Population L Ballerini, AE Fetit, S Wunderlich, R Lovreglio, S McGrory, ... Medical Image Understanding and Analysis, 400-409, 2020 | | 2020 |
| Retinal biomarker discovery for dementia in an elderly diabetic population AE Fetit, S Manivannan, S McGrory, L Ballerini, A Doney, TJ MacGillivray, ... Fetal, Infant and Ophthalmic Medical Image Analysis, 150-158, 2017 | | 2017 |
| Corrigendum: Mutations in genes encoding condensins cause microcephaly through decatenation failure at mitosis CA Martin, JE Murray, P Carroll, A Leitch, KJ MacKenzie, M Halachev, ... Genes & Development 31 (9), 953, 2017 | | 2017 |
| An Extensible Neuroimaging e-Repository for Clinical Trials of Paediatric Brain Tumours. AE Fetit, O Khan, S Afyouni, N Zarinabad, J Novak, AC Peet, TN Arvanitis Studies in health technology and informatics 213, 49, 2015 | | 2015 |
| Radiomics in paediatric neuro-oncology: MRI textural features as diagnostic and prognostic biomarkers AE Fetit University of Warwick, 2015 | | 2015 |
Theodoros N. ArvanitisProfessor of Digital Health Innovation and Director of the Institute of Digital Healthcare, WMGVerified email at warwick.ac.uk
