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<!DOCTYPE HTML>
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Nicola K Dinsdale</title>
<meta name="author" content="Nicola K Dinsdale">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
</head>
<body>
<table
style="width:100%;max-width:800px;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;">
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<tr style="padding:0px">
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<table
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<td style="padding:2.5%;width:63%;vertical-align:middle">
<p style="text-align:center">
<name>Explainable AI</name>
</p>
<p> Deep learning models are often regarded as black boxes, but this limits the ability to trust the decisions they make. Thus, methods are needed to enable understanding of the mechanisms behind the decision making process.
</p>
<p style="text-align:center">
<a href="index.html">Home</a>  / 
<a
href="harmonisation.html">Harmonisation</a>
 / 
<a href="segmentation.html">Segmentation</a>  / 
<a href="translation.html">Translation</a>  / 
<a href="privacy.html">Privacy</a>  / 
<a href="explainable.html">Explainable AI</a>
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<a href="images/characterising.png"><img style="width:100%;max-width:100%"
alt="profile photo" src="images/characterising.png" class="hoverZoomLink"></a>
</td>
</tr>
</tbody>
</table>
<table
style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;">
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<td style="padding:20px;width:100%;vertical-align:middle">
<p style="text-align:center">
<heading>Papers</heading>
</td>
</tr>
</tbody>
</table>
<table
style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;">
<tbody>
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<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/jay_paper.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2405.13235">
<papertitle>QAERTS: Geometric Transformation Uncertainty for Improving 3D Fetal Brain Pose Prediction from Freehand 2D Ultrasound Videos </papertitle>
</a>
<br>
<a href="https://reuben.ox.ac.uk/people/jayroop-ramesh#tab-4269921">Jayroop Ramesh</a>, <strong> Nicola K Dinsdale</strong>, the INTERGROWTH-21st Consortium, Pak-Hei Yeung, <a href="https://www.pmb.ox.ac.uk/person/dr-ana-namburete">Ana IL Namburete</a>
<br>
<em> MICCAI 2024 (Early Accept, Top 11%) </em>
<br>
<a href="https://arxiv.org/abs/2405.13235">Paper</a> / <a href="https://github.com/jayrmh/QAERTS.git">Code</a>
<p></p>
<p> We propose an uncertainty-aware deep learning model for automated 3D plane localization in 2D fetal brain images. </p>
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/US.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/pdf/2306.09858.pdf">
<papertitle>Prototype Learning for Explainable Regression </papertitle>
</a>
<br>
<a href="https://lindehesse.github.io/publications/">Linde S Hesse</a>, <strong> Nicola K Dinsdale </strong>, <a href="https://www.pmb.ox.ac.uk/person/dr-ana-namburete">Ana IL Namburete</a>
<br>
<em>IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024</em>
<br>
<a href="https://arxiv.org/pdf/2306.09858.pdf">Paper</a> / <a href="https://github.com/lindehesse/INSightR-Net">Code</a>
<p></p>
<p> In this work we present ExPeRT: an explainable prototype-based model specifically designed for regression tasks.</p>
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/characterising.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://www.medrxiv.org/content/10.1101/2023.06.22.23291592v1.full.pdf">
<papertitle>Characterizing personalized neuropathology in dementia and mild cognitive impairment with explainable artificial
intelligence </papertitle>
</a>
<br>
Esten H. Leonardsen, Karin Persson, Edvard Grødem, <strong> Nicola K Dinsdale </strong>, ... , Thomas Wolfers, Lars T. Westlye, Yunpeng Wang
<br>
<em>npj Digital Medicine, 2024</em>
<br>
<a href="https://www.medrxiv.org/content/10.1101/2023.06.22.23291592v1.full.pdf">Paper</a>
<p></p>
<p> 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. </p>
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/features.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://www.sciencedirect.com/science/article/pii/S0896627322008170?dgcid=author">
<papertitle>Challenges for machine learning in clinical translation of big data imaging studies</papertitle>
</a>
<br>
<strong> Nicola K Dinsdale </strong>, <a href="http://emmabluemke.com/">Emma Bluemke</a>, <a href="https://www.ndcn.ox.ac.uk/team/vaanathi-sundaresan">Vaanathi Sundaresan</a>, <a href="https://www.ndcn.ox.ac.uk/team/mark-jenkinson">Mark Jenkinson</a>, <a href="https://www.ndcn.ox.ac.uk/team/stephen-smith">Steve Smith</a>, <a href="https://www.pmb.ox.ac.uk/person/dr-ana-namburete">Ana IL Namburete</a>
<br>
<em>Neuron </em>, 2022
<br>
<a href="https://www.sciencedirect.com/science/article/pii/S0896627322008170?dgcid=author">Paper</a> / <a href="https://github.com/nkdinsdale/challenges_review">Code</a>
<p></p>
<p> Review article, explores: data availability, interpretability, model bias and data privacy.</p>
</td>
</tr>
<tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/age_pred.png'
width="160"
height="150">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://www.sciencedirect.com/science/article/pii/S1053811920308867">
<papertitle> Learning patterns of the ageing brain in MRI using deep convolutional networks </papertitle>
</a>
<br>
<strong> Nicola K Dinsdale </strong>, <a href="http://emmabluemke.com/">Emma Bluemke</a>, <a href="https://www.ndcn.ox.ac.uk/team/stephen-smith">Steve Smith</a>, Zobair Arya, Diego Vidaurre, <a href="https://www.ndcn.ox.ac.uk/team/mark-jenkinson">Mark Jenkinson</a>, <a href="https://www.pmb.ox.ac.uk/person/dr-ana-namburete">Ana IL Namburete</a>
<br>
<em>Neuroimage</em>, 2021
<br>
<a href="https://nkdinsdale.github.io/agepred_project/">Project Page</a> / <a href="https://www.sciencedirect.com/science/article/pii/S1053811920308867">Paper</a> / <a href="https://github.com/nkdinsdale/LearningPatternsofAgeing">Code</a>
<p></p>
<p> Development of age prediction model using data from the UK Biobank, and exploration of correlations with UK Biobank Variables. We also explore the effect of registration on the model.</p>
</td>
</tr>
</tbody>
</table>
<br>
<p style="text-align:right;font-size:small;">
The template of this webpage is from <a
href="https://github.com/jonbarron/jonbarron_website">source
code</a>.
</p>
</td>
</tr>
</tbody>
</table>
</body>
</html>