Dr Nicola Dinsdale
I am a Postdoctoral Research Associate in the Oxford Machine Learning in NeuroImaging Group (OMNI) in the Department of Computer Science, working with Prof Ana Namburete. My background is in Engineering (Brasenose, 2013) and I completed my DPhil at the Wellcome Centre for Integrative Neuroimaging, exploring how we can optimise neural networks for large scale analysis of MRI of the brain.
I am particularly interested in method development to enable deep learning methods to be applied in the clinic, such as overcoming biases associated with scanner, creating models which are appropriate for different demographics, and maintaining individual privacy.
I am originally from Barnsley, South Yorkshire, where I attended the local state comprehensive, and am passionate about improving access to higher education.
Full publication list: https://nkdinsdale.github.io/nkdinsdale/
Selected publications:
Nicola K. Dinsdale, Mark Jenkinson, Ana I.L. Namburete, STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation, Medical Image Analysis, 2022 - https://doi.org/10.1016/j.media.2022.102583.
Nicola K. Dinsdale, Mark Jenkinson, Ana I.L. Namburete, Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal, NeuroImage, 2021 - https://doi.org/10.1016/j.neuroimage.2020.117689.
Nicola K. Dinsdale, Emma Bluemke, Stephen M. Smith, Zobair Arya, Diego Vidaurre, Mark Jenkinson, Ana I.L. Namburete, Learning patterns of the ageing brain in MRI using deep convolutional networks, NeuroImage, 2021- https://doi.org/10.1016/j.neuroimage.2020.117401.
Group Website: https://omni.cs.ox.ac.uk/
Dr Nicola Dinsdale
I am a Postdoctoral Research Associate in the Oxford Machine Learning in NeuroImaging Group (OMNI) in the Department of Computer Science, working with Prof Ana Namburete. My background is in Engineering (Brasenose, 2013) and I completed my DPhil at the Wellcome Centre for Integrative Neuroimaging, exploring how we can optimise neural networks for large scale analysis of MRI of the brain.
I am particularly interested in method development to enable deep learning methods to be applied in the clinic, such as overcoming biases associated with scanner, creating models which are appropriate for different demographics, and maintaining individual privacy.
I am originally from Barnsley, South Yorkshire, where I attended the local state comprehensive, and am passionate about improving access to higher education.
Full publication list: https://nkdinsdale.github.io/nkdinsdale/
Selected publications:
Nicola K. Dinsdale, Mark Jenkinson, Ana I.L. Namburete, STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation, Medical Image Analysis, 2022 - https://doi.org/10.1016/j.media.2022.102583.
Nicola K. Dinsdale, Mark Jenkinson, Ana I.L. Namburete, Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal, NeuroImage, 2021 - https://doi.org/10.1016/j.neuroimage.2020.117689.
Nicola K. Dinsdale, Emma Bluemke, Stephen M. Smith, Zobair Arya, Diego Vidaurre, Mark Jenkinson, Ana I.L. Namburete, Learning patterns of the ageing brain in MRI using deep convolutional networks, NeuroImage, 2021- https://doi.org/10.1016/j.neuroimage.2020.117401.
Group Website: https://omni.cs.ox.ac.uk/