PhD position: Graph Neural Networks for morpho-functional analysis and comparison of brain structures
29 Mars 2022
Catégorie : Doctorant
Open position in Tours (France)
Very short description: In the context of a multidisciplinary collaboration between data/computer scientists and neurobiologists, the proposed thesis aims to create new methods and algorithms for anatomical and functional analysis and comparison of brain structures using recent deep neural networks techniques dedicated to graphs (GNN, geometric deep learning ...).
Supervisors: Jean-Yves Ramel (LIFATUniversité de Tours - Elodie Chaillou (INRAE PRC) in collaboration with iBrain, INSERM (C.Destrieux, F. Anderson)
Nowadays,the development of brain imaging methods generates a considerable amount of morphological and functional data. However, their exploration and comparison over time for an individual (development and aging), between individuals (variability within the species), and even more so between different species have beendoneonly partially. We propose to model these data in the form of graphs, then to use recent approaches of artificial intelligence
to better analyze them.
This approach has already been initiatedbya multidisciplinary consortium of researchers in neuroanatomy,biology and computer science as well as neurosurgeons during the Regional projectsNeuroGéoandNeuro2Co(LIFAT, INRAE, INSERM).It led to the creation ofSILA3D,a software platform(in free access)allowing the representation of anatomo-functional data in the form ofgraphs thanks to an interactive semantic segmentation of images.
In this context, the proposed thesis aims to create new algorithms for anatomical and functional analysis and comparison of brainstructuresusingrecentdeepneural networkstechniques dedicated tographs (GNN, geometric deep learning ...).
The general objectives of this thesis are:
-To specify different strategies for modelingthebraindata as graphs. For this, morphological and functional data from different imaging modalities, including structural MRI and tractography,will be combined using different approaches to be defined.
-ToInvestigate differences between individuals (human brainstem variability) and over time (monitoring lamb brain development from birth to adulthood). The PhD student will propose several graph comparison methods exploiting recent advances in Deep Learning on Graphs (GNN).
The scientific challenges associated with these objectives are (1) to develop new graph-based deep learning methods for the detection and classification of particular substructures in an encephalon (semi-supervised classification of nodes); (2) to develop new graph-based deep learning methods for the comparison, discrimination, and
classification of encephalon (supervised or unsupervised classification of graphs) [4,11].
Candidates must have an MSc or engineering degree in a field related to computer science or applied mathematics, with strong programming skills (in particular with deep learning frameworks). Experience with medical image analysis or brain analysis will be a plus. Candidates are expected to have abilities to write scientific reports and communicate
research results at conferences in English.
Information and application
Applications should include the following documents in electronic format: i) A short motivation letter stating why you are interested in this project, ii) A detailed CV describing your past education and research background related to the position.iii) Thetranscripts for master degrees. iv)The contact information for references (do not include the reference letters with your applications as we will only ask for the reference letters for short-listed candidates).
Please send your application package to firstname.lastname@example.org and email@example.com
A first selection will occur and then interviews will be proposed between April and the end of May. The position will start in October 2022 with a salary of 1975 euros gross/month (legal amount for doctoral contracts in France) and will be located in Tours, France.