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Statistical and machine learning methods for longitudinally measured image datasets


Key facts

Type of research degree
Application deadline
Friday 17 April 2020
Project start date
Thursday 1 October 2020
Country eligibility
UK and EU
Competition funded
Source of funding
Research council
Professor Alex Frangi and Professor Jeanne Houwing-Duistermaat
Additional supervisors
Dr Haiyan Liu
School of Computing, School of Mathematics
Research groups/institutes
Statistical methodology and probability, Statistics
<h2 class="heading hide-accessible">Summary</h2>

Large datasets are nowadays everywhere. In medicine examples include imaging and omics datasets; in finance large dataset comprises information about credit card use; another example is twitter data. It is often relevant to identify patterns across datasets, e.g. changes in images over time, or to link datasets with an outcome variable such as images of the knee and osteoarthritis for knee images, or brain images and Alzheimer. A set of all relevant patterns (relationships) reduces the dimension of the original data.

<h2 class="heading hide-accessible">Full description</h2>

<p>A type of methods which is often used for dimension reduction is latent variable approaches such as principal components and partial least squares methods. These latent variables are linear combinations from the original variables and represent the relevant information. A second class of dimension reduction techniques is variable selection. How to combine these two approaches is a topic of current research.</p> <p>This project is motivated by longitudinally measured images of knees in patient with osteoarthritis. Osteoarthritis is a common disease in the elderly. It is a condition in which the natural cushioning between joints -- cartilage -- wears away. When this happens, the bones of the joints rub more closely against one another with less of the shock-absorbing benefits of cartilage. The rubbing results in pain, swelling, stiffness, and decreased ability to move. Surgery might be needed to replace the knee. There are images for seven time points for almost 5K patients. Detailed descriptions of the data can be found <a href="">here</a>.</p> <p>We aim to develop statistical and machine learning methods which are able to cluster patients which have similar knee profiles over time (unsupervised clustering) and to predict changes in the images of the knee predicting severe disease outcomes (classification). To deal with the high dimensionality of longitudinally observed two dimensional images, we propose methods which summarize the images over time in curves over time. And these curves over time represent the changes in the images and can be used in supervised or unsupervised clustering.&nbsp;</p> <p>In this project we will focus on knee images and osteoarthritis. However these methods will be widely applicable for example images of the brain and Alzheimer.</p>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="">University&#39;s website</a>. Please select &ldquo;PhD Statistics Full-time&rdquo; as your Planned Course of Study, and state clearly in the research information section&nbsp;that the research degree you wish to be considered for is &ldquo;Statistical and machine learning methods for longitudinally measured image datasets&rdquo; as well as <a href="">Professor Jeanne Houwing-Duistermaat</a>&nbsp;as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

<h2 class="heading">Funding on offer</h2>

<p><strong>UK/EU</strong>&nbsp;&ndash;&nbsp;Engineering &amp; Physical Sciences Research Council Studentship&nbsp;for 3.5 years. A full standard studentship consists of academic fees (&pound;4,600 in Session 2020/21), together with a maintenance grant&nbsp;paid at standard Research Council rates (&pound;15,285&nbsp;in Session 2020/21). UK applicants will be eligible for a full award paying tuition fees and maintenance. European Union applicants will be eligible for an award paying tuition fees only, except in exceptional circumstances, or where residency has been established for more than 3 years prior to the start of the course.&nbsp;Funding is awarded on a competitive basis.</p>

<h2 class="heading">Contact details</h2>

<p>For further information please contact Doctoral College Admissions<br /> e:&nbsp;<a href=""></a>, t: +44 (0)113 343 5057.</p>

<h3 class="heading heading--sm">Linked funding opportunities</h3>
<h3 class="heading heading--sm">Linked research areas</h3>