Nicola K Dinsdale

Translation of Deep Learning

Deep learning has shown great promise in the research domain, but significant barriers still exist which limit the clinical translatability of deep learning models.

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Papers

	Challenges for machine learning in clinical translation of big data imaging studies Nicola K Dinsdale , Emma Bluemke, Vaanathi Sundaresan, Mark Jenkinson, Steve Smith, Ana IL Namburete Neuron , 2022 Paper / Code Review article, explores: data availability, interpretability, model bias and data privacy.
	Automated quality assessment using appearance-based simulations and hippocampus segmentation on Low-field paediatric brain MR images Vaanathi Sundaresan, Nicola K Dinsdale Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance Challenge, 2024 Paper / Code Winner of LISA Challenge 2024 @ MICCAI 2024
	Automated quality assessment using appearance-based simulations and hippocampus segmentation on Low-field paediatric brain MR images Madeleine K Wyburd, Nicola K Dinsdale, V. Kyriakopoulou, L. Venturini, A. Uus, J. Matthew, E. Skelton, L. Zöllei, J. Hajnal, Ana IL Namburete EP04.02: Fetal brain imaging in 3D: a direct comparison between same day MRI and ultrasound volumetric measures Paper Fetal brain structures appear radically different when imaged using ultrasound (US) and Magnetic Resonance Imaging (MRI). Using same-day US and MRI we investigated differences in volume measurements derived from each modality.
	Is Your Style Transfer Doing Anything Useful? An Investigation Into Hippocampus Segmentation and the Role of Preprocessing Hoda Kalabizadeh, Ludovica Griffanti, Pak-Hei Yeung, Natalie Voets, Grace Gillis, Clare E Mackay, Ana IL Namburete, Nicola K Dinsdale, Konstantinos Kamnitsas Machine Learning in Clinical Neuroimaging, 2024 Paper We investigated the performance of segmentation models trained on research data that were style-transferred to resemble clinical scans. Our results highlighted the importance of intensity normalisation methods in MRI segmentation, and their relation to domain shift and style-transfer.
	QAERTS: Geometric Transformation Uncertainty for Improving 3D Fetal Brain Pose Prediction from Freehand 2D Ultrasound Videos Jayroop Ramesh, Nicola K Dinsdale, the INTERGROWTH-21st Consortium, Pak-Hei Yeung, Ana IL Namburete MICCAI 2024 (Early Accept, Top 11%) Paper / Code We propose an uncertainty-aware deep learning model for automated 3D plane localization in 2D fetal brain images.
	Characterizing personalized neuropathology in dementia and mild cognitive impairment with explainable artificial intelligence Esten H. Leonardsen, Karin Persson, Edvard Grødem, Nicola K Dinsdale , ... , Thomas Wolfers, Lars T. Westlye, Yunpeng Wang Medrxiv, 2023 Paper We trained convolutional neural nets to differentiate patients with dementia from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that patterns recognized by the model corroborate existing knowledge of neuropathology in dementia.
	The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects Erica Balboni, Luca Nocetti, Chiara Carbone, Nicola K Dinsdale , Maurilio Genovese, Gabriele Guidi, Marcella Malagoli, Annalisa Chiari, Ana IL Namburete, Mark Jenkinson, Giovanna Zamboni, Human Brain Mapping 2022 Paper / Code Exploration of the use of my SWANS algorithm for clinical data, across disease groups - SWANS first presented at MICCAI 2019

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