| Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal NK Dinsdale, M Jenkinson, AIL Namburete NeuroImage 228, 117689, 2021 | 121 | 2021 |
| Learning patterns of the ageing brain in MRI using deep convolutional networks NK Dinsdale, E Bluemke, SM Smith, Z Arya, D Vidaurre, M Jenkinson, ... NeuroImage 224, 117401, 2021 | 101 | 2021 |
| Challenges for machine learning in clinical translation of big data imaging studies NK Dinsdale, E Bluemke, V Sundaresan, M Jenkinson, SM Smith, ... Neuron 110 (23), 3866-3881, 2022 | 25 | 2022 |
| Spatial warping network for 3D segmentation of the hippocampus in MR images NK Dinsdale, M Jenkinson, AIL Namburete Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 23 | 2019 |
| TEDS-Net: enforcing diffeomorphisms in spatial transformers to guarantee topology preservation in segmentations MK Wyburd, NK Dinsdale, AIL Namburete, M Jenkinson International Conference on Medical Image Computing and Computer-Assisted …, 2021 | 22 | 2021 |
| Unlearning scanner bias for MRI harmonisation in medical image segmentation NK Dinsdale, M Jenkinson, AIL Namburete Medical Image Understanding and Analysis: 24th Annual Conference, MIUA 2020 …, 2020 | 22 | 2020 |
| A 2-step deep learning method with domain adaptation for multi-centre, multi-vendor and multi-disease cardiac magnetic resonance segmentation J Corral Acero, V Sundaresan, N Dinsdale, V Grau, M Jenkinson Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC …, 2021 | 18 | 2021 |
| Fedharmony: Unlearning scanner bias with distributed data NK Dinsdale, M Jenkinson, AIL Namburete International Conference on Medical Image Computing and Computer-Assisted …, 2022 | 12 | 2022 |
| Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal. NeuroImage, 228, Article 117689 NK Dinsdale, M Jenkinson, AIL Namburete | 11 | 2020 |
| Stamp: simultaneous training and model pruning for low data regimes in medical image segmentation NK Dinsdale, M Jenkinson, AIL Namburete Medical Image Analysis 81, 102583, 2022 | 10 | 2022 |
| The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects E Balboni, L Nocetti, C Carbone, N Dinsdale, M Genovese, G Guidi, ... Human Brain Mapping 43 (11), 3427-3438, 2022 | 10 | 2022 |
| Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images V Sundaresan, G Zamboni, NK Dinsdale, PM Rothwell, L Griffanti, ... Medical Image Analysis 74, 102215, 2021 | 10 | 2021 |
| Learning patterns of the ageing brain in MRI using deep convolutional networks. NeuroImage, 224, Article 117401 NK Dinsdale, E Bluemke, SM Smith, Z Arya, D Vidaurre, M Jenkinson, ... | 10 | 2020 |
| Sfharmony: Source free domain adaptation for distributed neuroimaging analysis NK Dinsdale, M Jenkinson, AIL Namburete Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 3 | 2023 |
| Prototype learning for explainable brain age prediction LS Hesse, NK Dinsdale, AIL Namburete Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 1 | 2024 |
| Characterizing personalized neuropathology in dementia and mild cognitive impairment with explainable artificial intelligence EH Leonardsen, K Persson, E Grødem, N Dinsdale, T Schellhorn, JM Roe, ... | 1 | 2023 |
| Omni-supervised domain adversarial training for white matter hyperintensity segmentation in the uk biobank V Sundaresan, NK Dinsdale, M Jenkinson, L Griffanti 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 1-4, 2022 | 1 | 2022 |
| Optimising convolutional neural networks for large-scale neuroimaging studies NK Dinsdale University of Oxford, 2021 | 1 | 2021 |
| UniFed: A unified deep learning framework for segmentation of partially labelled, distributed neuroimaging data NK Dinsdale, M Jenkinson, AIL Namburete bioRxiv, 2024.02. 05.578912, 2024 | | 2024 |
| Model Pruning for Generalisability Across MRI Imaging Sites NK Dinsdale, M Jenkinson, AIL Namburete ISMRM, 2022 | | 2022 |
Mark JenkinsonProfessor of NeuroimagingVerified email at adelaide.edu.au
