Vous êtes ici : Accueil » Kiosque » Annonce

Identification

Identifiant: 
Mot de passe : 

Mot de passe oublié ?
Détails d'identification oubliés ?

Annonce

8 décembre 2018

Master 2 internship position in medical imaging and machine Learning for Alzheimer's disease diagnsois


Catégorie : Stagiaire


The university of Poitiers and the XLIM institute (in collaboration with CHU of Poitiers) are offering one master 2 internship position of 6 months in medical imaging and Machine Learning for Alzheimer's disease diagnosis.

Supervisors : Dr. Olfa Ben-Ahmed (XLIM, university of Potiers) and Dr. Carole Guillevin (DACTIM-LMA, university of Poitiers) 

 

Subject :classification of Alzheimer's disease subjects using Spectroscopy data

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 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 MRS data. Data used in this internship are provided by CHU of Poitiers.

Objectives:

-Develop and evaluate machine learning algorithms for AD spectroscopy data classification

-Propose solution for learning from few spectroscopy data for AD subject’s classification

-Jointly Investigate the structural and metabolic changes associated with incipient AD pathology to improve MCI subject’s detection.

References:

[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

[2] Sarraf, S., Tofighi, G.,. DeepAD: Alzheimer′s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI. bioRxiv 2016

[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

[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.

Requirements

-MS student in Computer Science, image/signal processing, computer vision, machine learning or related streams.

-Strong knowledge in image processing and machine learning
-Excellent programming skills (Python, C++, MATLAB)

Salary: 560€/ Month

Application: interested candidates should send their CV and a cover letter to olfa.ben.ahmed@univ-poitiers.fr

 

Dans cette rubrique

(c) GdR 720 ISIS - CNRS - 2011-2018.