| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 2166 | 2018 |
| Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress A Lowe, N Harrison, AP French Plant methods 13 (1), 80, 2017 | 622 | 2017 |
| Colocalization of fluorescent markers in confocal microscope images of plant cells AP French, S Mills, R Swarup, MJ Bennett, TP Pridmore Nature protocols 3 (4), 619-628, 2008 | 408 | 2008 |
| Deep machine learning provides state-of-the-art performance in image-based plant phenotyping MP Pound, JA Atkinson, AJ Townsend, MH Wilson, M Griffiths, ... Gigascience 6 (10), gix083, 2017 | 381 | 2017 |
| Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism LR Band, DM Wells, A Larrieu, J Sun, AM Middleton, AP French, ... Proceedings of the National Academy of Sciences 109 (12), 4668-4673, 2012 | 357 | 2012 |
| Leaf segmentation in plant phenotyping: a collation study H Scharr, M Minervini, AP French, C Klukas, DM Kramer, X Liu, I Luengo, ... Machine vision and applications 27, 585-606, 2016 | 329 | 2016 |
| RootNav: navigating images of complex root architectures MP Pound, AP French, JA Atkinson, DM Wells, MJ Bennett, T Pridmore Plant physiology 162 (4), 1802-1814, 2013 | 275 | 2013 |
| High-throughput quantification of root growth using a novel image-analysis tool A French, S Ubeda-Tomás, TJ Holman, MJ Bennett, T Pridmore Plant physiology 150 (4), 1784-1795, 2009 | 253 | 2009 |
| Root branching toward water involves posttranslational modification of transcription factor ARF7 B Orosa-Puente, N Leftley, D Von Wangenheim, J Banda, AK Srivastava, ... Science 362 (6421), 1407-1410, 2018 | 235 | 2018 |
| Systems Analysis of Auxin Transport in the Arabidopsis Root Apex LR Band, DM Wells, JA Fozard, T Ghetiu, AP French, MP Pound, ... The Plant Cell 26 (3), 862-875, 2014 | 232 | 2014 |
| Deep learning for multi-task plant phenotyping MP Pound, JA Atkinson, DM Wells, TP Pridmore, AP French Proceedings of the IEEE International Conference on Computer Vision …, 2017 | 217 | 2017 |
| Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields J Gao, AP French, MP Pound, Y He, TP Pridmore, JG Pieters Plant methods 16, 1-12, 2020 | 187 | 2020 |
| Weakly supervised object detection with posterior regularization H Bilen, M Pedersoli, T Tuytelaars British Machine Vision Conference 3, 4, 2014 | 164 | 2014 |
| Automated recovery of three-dimensional models of plant shoots from multiple color images MP Pound, AP French, EH Murchie, TP Pridmore Plant physiology 166 (4), 1688-1698, 2014 | 158 | 2014 |
| Sequential induction of auxin efflux and influx carriers regulates lateral root emergence B Péret, AM Middleton, AP French, A Larrieu, A Bishopp, M Njo, DM Wells, ... Molecular systems biology 9 (1), 699, 2013 | 135 | 2013 |
| RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures R Yasrab, JA Atkinson, DM Wells, AP French, TP Pridmore, MP Pound GigaScience 8 (11), giz123, 2019 | 126 | 2019 |
| Behavioural changes in dairy cows with lameness in an automatic milking system GG Miguel-Pacheco, J Kaler, J Remnant, L Cheyne, C Abbott, AP French, ... Applied Animal Behaviour Science 150, 1-8, 2014 | 120 | 2014 |
| Approaches to three-dimensional reconstruction of plant shoot topology and geometry JA Gibbs, M Pound, AP French, DM Wells, E Murchie, T Pridmore Functional Plant Biology 44 (1), 62-75, 2016 | 113 | 2016 |
| CellSeT: novel software to extract and analyze structured networks of plant cells from confocal images MP Pound, AP French, DM Wells, MJ Bennett, TP Pridmore The Plant Cell 24 (4), 1353-1361, 2012 | 98 | 2012 |
| SuRVoS: super-region volume segmentation workbench I Luengo, MC Darrow, MC Spink, Y Sun, W Dai, CY He, W Chiu, ... Journal of Structural Biology 198 (1), 43-53, 2017 | 90 | 2017 |
Tony PridmoreUniversity of NottinghamEmail verificata su cs.nott.ac.uk