Nicola K. Dinsdale

According to our database, Nicola K. Dinsdale authored at least 15 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2024

Prototype Learning for Explainable Brain Age Prediction.

Linde S. Hesse, Nicola K. Dinsdale, Ana I. L. Namburete

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

2023

Prototype Learning for Explainable Regression.

Linde S. Hesse, Nicola K. Dinsdale, Ana I. L. Namburete

CoRR, 2023

SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis.

Nicola K. Dinsdale, Mark Jenkinson, Ana I. L. Namburete

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation.

Nicola K. Dinsdale, Mark Jenkinson, Ana I. L. Namburete

Medical Image Anal., 2022

FedHarmony: Unlearning Scanner Bias with Distributed Data.

Nicola K. Dinsdale, Mark Jenkinson, Ana I. L. Namburete

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

Omni-Supervised Domain Adversarial Training for White Matter Hyperintensity Segmentation in the UK Biobank.

Vaanathi Sundaresan, Nicola K. Dinsdale, Mark Jenkinson, Ludovica Griffanti

Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022

2021

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.

Nicola K. Dinsdale, Mark Jenkinson, Ana I. L. Namburete

NeuroImage, 2021

Learning patterns of the ageing brain in MRI using deep convolutional networks.

Nicola K. Dinsdale, Emma Bluemke, Stephen M. Smith, Zobair Arya, Diego Vidaurre, Mark Jenkinson, Ana I. L. Namburete

NeuroImage, 2021

Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images.

Vaanathi Sundaresan, Giovanna Zamboni, Nicola K. Dinsdale, Peter M. Rothwell, Ludovica Griffanti, Mark Jenkinson

Medical Image Anal., 2021

Challenges for machine learning in clinical translation of big data imaging studies.

Nicola K. Dinsdale, Emma Bluemke, Vaanathi Sundaresan, Mark Jenkinson, Stephen M. Smith, Ana I. L. Namburete

CoRR, 2021

TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations.

Madeleine K. Wyburd, Nicola K. Dinsdale, Ana I. L. Namburete, Mark Jenkinson

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

2020

Unlearning Scanner Bias for MRI Harmonisation in Medical Image Segmentation.

Nicola K. Dinsdale, Mark Jenkinson, Ana I. L. Namburete

Proceedings of the Medical Image Understanding and Analysis - 24th Annual Conference, 2020

Unlearning Scanner Bias for MRI Harmonisation.

Nicola K. Dinsdale, Mark Jenkinson, Ana I. L. Namburete

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

A 2-Step Deep Learning Method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation.

Jorge Corral Acero, Vaanathi Sundaresan, Nicola K. Dinsdale, Vicente Grau, Mark Jenkinson

Proceedings of the Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges, 2020

2019

Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images.

Nicola K. Dinsdale, Mark Jenkinson, Ana I. L. Namburete

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019