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.
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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