02/06/2020
The Human Resources Strategy for Researchers

PhD contract in the field of Computer science and mathematics financed during 3 years by the University Clermont Auvergne

This job offer has expired


  • ORGANISATION/COMPANY
    Université Clermont Auvergne
  • RESEARCH FIELD
    Computer science
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    28/06/2020 00:00 - Europe/Brussels
  • LOCATION
    France › AUBIERE
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35 H
  • OFFER STARTING DATE
    01/10/2020
  • REFERENCE NUMBER
    UCA/ANR/008
  • IS THE JOB RELATED TO STAFF POSITION WITHIN A RESEARCH INFRASTRUCTURE?
    Yes

Subject: Robust graph-based deep learning; application to (bio)medical

image analysis

Supervisor: Prof. Antoine Vacavant

Laboratory: Institut Pascal, UMR6602 UCA/SIGMA/CNRS

Email and phone: antoine.vacavant@uca.fr ; (+33) [0]4 71 09 90 82

Co-advisor(s):

Abstract (up to 10 lines):

Numerous deep learning (DL) approaches have been proposed for

image analysis tasks in computer-aided (bio)medical applications.

Images are generally considered as examples that are learned directly

to the system, then able to predict specific decisions (e.g. image

segmentation or classification). Nevertheless, image data can be

preferably represented with discrete structures as graphs. This PhD

deals with the development of novel DL methods based on graphs, by

robust approaches, i.e. they are able to resist to inherent image data

noise. Within a multidisciplinary team, composed of scientific members

and medical doctors, several applications can be considered, mostly

related to the 3D reconstruction of vascular networks.

Skills: Good mathematical background; solid skills in programming;

good knowledge of machine/deep learning and image

analysis/processing preferable; experience in medical or biomedical

applications appreciated

Keywords: Deep learning; Graphs; Robustness; Image analysis;

Biomedical and medical applications

Description (up to 1 page):

An extremely common methodology for analyzing image data today in

computer-aided (bio)medical applications is the development of deep

neural networks and other related deep learning (DL) architectures

[1]. These networks generally take directly images as inputs during

learning phase, and are then able to predict specific decisions (e.g.

image segmentation or classification) with new image samples.

Recently, a relevant work has been done towards generalizing DL to

graphs [2,3]. Considering graphs is an important task with many

potential applications, such as analyzing data on networks, computer

vision, natural language processing, medical applications, etc.

Additionally, image data can be preferably represented with discrete

structures as graphs. As an illustration of the interest in learning

graphs by neural structures, we can note that 2 libraries, named NSL

(Neural Structured Learning) and Deep Graph Library, have been

developed for this purpose, based on the reputed Tensor Flow

framework [4,5].

This PhD deals with the development of novel DL methods based on

graphs, by robust approaches, i.e. they are able to resist to inherent

image data noise [6]. We would like to investigate further Graph

Convolutional Networks (GCN) [2] and their application to 3D vascular

segmentation and reconstruction, which is a very recent concern [7,8].

It is even possible to combine GCN and standard Convolutional Neural

Networks (CNN) to learn and represent both structural and image

intensity information into a single DL framework.

The PhD candidate will join the CaVITI multidisciplinary team (Cardio-

Vascular Interventional Therapy and Imaging), hosted at Le Puy-en-

Velay. It is composed of scientific members (skilled in computer

science, signal/image processing, machine learning, etc.) and medical

doctors (specialized in interventional radiology, hepatology, liver

surgery, etc.) from University Hospital (CHU) of Clermont-Ferrand. This

PhD will be associated to ongoing research projects coordinated by the

host team, in particular the ANR R-Vessel-X [9] and PHC/Polonium

DeepVesselNets projects. Main applications would be the 3D

reconstruction of liver vessels from MRI (Magnetic Resonance Imaging)

sequences or from microscopic imaging of small animals (synchrotron

acquisitions or μMRI), but the methodologies developed in this PhD

could be applied to other organs, e.g. brain vasculature or lung

airwaves.

References (up to ½ page):

[1] D. Shen et al. Deep Learning in Medical Image Analysis. Annual

Review of Biomedical Engineering, vol. 19, pp. 221-248, 2017.

[2] T.N. Kipf and M. Welling. Semi-supervised classification with

graph convolutional networks. In International Conference on

Learning Representations 2017.

[3] F. Wu et al. Simplifying Graph Convolutional Networks. In

International Conference on Machine Learning, 2019.

[4] https://www.tensorflow.org/neural_structured_learning

[5] https://www.dgl.ai/

[6] A. Fawzi et al. The robustness of deep networks: A geometrical

perspective. IEEE Signal Processing Magazine, vol. 34, pp. 50-62, 2017.

[7] H. Yang et al. CPR-GCN: Conditional Partial-Residual Graph

Convolutional Network in Automated Anatomical Labeling of Coronary

Arteries. arXiv:2003.08560, 2020.

[8] J.M. Wolterink et al. Graph Convolutional Networks for Coronary

Artery Segmentation in Cardiac CT Angiography. In International

Workshop on Graph Learning in Medical Imaging, 2019.

[9] http://tgi.ip.uca.fr/r-vessel-x/

How to candidate?

Contact the supervisor

 
 
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Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Other: Master Degree or equivalent
Work location(s)
1 position(s) available at
Pascal Institute (IP)
France
Région Auvergne Rhône-Alpes
AUBIERE
63178
Campus Universitaire des Cézeaux TSA 60026 CS 60026 4, Avenue Blaise Pascal

Open, Transparent, Merit based Recruitment procedures of Researchers (OTM-R)

Know more about it at Université Clermont Auvergne

Know more about OTM-R

EURAXESS offer ID: 528425

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