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The university of Poitiers and the XLIM institute are offering an
internship position of 6 months in medical imaging and machine
learning for Alzheimer's disease diagnosis.<br>
<br>
<b>Title :</b> Classification of Alzheimer’s disease subjects
using Spectroscopy data<br>
<br>
<b>Scientific context:</b><br>
Alzheimer’s disease (AD) is the most comment form of dementia.
Neuroimaging data is an integral part of the clinical assessment
providing a way for clinicians to detect brain abnormalities for
AD diagnosis. Structural MRI with machine learning techniques has
been widely studied to assess brain atrophy for AD detection and
prediction [1][2]. In addition to structural changes, metabolic
changes in some brain regions could be a good biomarker for an
early AD [3]. Recently, Magnetic Resonance Spectroscopy (MRS)
have been proved to be effective to quantify certain brain
metabolites in vivo [4]. The proposed internship aims in testing
and evaluating the effectiveness of machine learning techniques
for single subject level classification of individuals affected by
different stages of AD (healthy elderly subjects, Mild Cognitive
Impairment (MCI) and AD subjects) based on 1H MRS data. Data used
in this internship are provided by CHU of Poitiers.<br>
<br>
<b>Objectives:</b><br>
-Evaluate and compare several machine learning algorithms for AD
spectroscopy data classification<br>
-Propose solution for learning from few data of spectroscopy data
for AD subject’s classification.<br>
-Jointly Investigate the structural and metabolic changes
associated with incipient AD pathology to improve MCI subject’s
detection.<br>
<br>
<b>References:</b><br>
[1] Olfa Ben Ahmed et al "Recognition of Alzheimer's Disease and
Mild Cognitive Impairment with multimodal image-derived biomarkers
and Multiple Kernel Learning", International Journal
Neurocomputing, vol. 220, p. 98-110, Elsevier 2017<br>
[2] Sarraf, S., Tofighi, G.,. DeepAD: Alzheimer′s Disease
Classification via Deep Convolutional Neural Networks using MRI
and fMRI. bioRxiv 2016<br>
[3] Wang Z, Zhao C, Yu L, et al Regional metabolic changes in the
hippocampus and posterior cingulated area detected with 3-Tesla
magnetic resonance spectroscopy in patients with mild cognitive
impairment and Alzheimer's disease. Acta Radiol 2009;50:312–19<br>
[4] Pedro J Modrego et al. Magnetic resonance spectroscopy in the
prediction of early conversion from amnestic mild cognitive
impairment to dementia: a prospective cohort study. BMJ Open 1,
e000007.<br>
<br>
<b>Key Words</b>: Alzheimer, MRI, spectroscopy, Artificial
Intelligence, Machine learning, information fusion, classification<br>
<br>
<b>Skills:</b><br>
MS student in Computer Science, Image and signal processing,
machine learning or related streams.<br>
Strong knowledge in at least one of the following fields is
required:<br>
<br>
• good image processing and machine learning knowledge<br>
• mathematical understanding of the formal background<br>
• excellent programming skills (Python, C++, MATLAB)<br>
• biomedical applications would be appreciated</div>
<div class="gmail_quote"><br>
<b>Salary</b>: 560€/ Month<br>
<br>
<b>Application: </b>interested candidates should send their CV
and a cover letter to <a
href="mailto:olfa.ben.ahmed@univ-poitiers.fr" target="_blank">olfa.ben.ahmed@univ-poitiers.<wbr>fr</a>
<br>
<br>
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<pre cols="72">Dr. Olfa BEN AHMED
Associate professor
XLIM - University of Poitiers</pre>
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