AUTOMATIC CEPHALOMETRIC ANALYSIS WITH DEEP LEARNING
Recrutement: 
Recrutement en cours/passé: 
Recrutement en cours
Periode: 
2019
  • Application deadline: November 30th, 2018
  • Beginning of the internship: February 4th, 2019
  • Duration: 6 months (longer duration possible condition to an agreement with the University/School)
  • Financial support: according to the competencies and the motivation of the candidate
  • Locations of the internships: Lyon, France

More details in the pdf file

Keywords:

Image Classification, Medical Image Analysis, Machine Learning (ML), Deep Learning (DL),Generative Adversarial Networks (GAN), Hidden Markov Model – HMM, Multiple Instance Learning (MIL),Convolutional Neural Networks (CNN), U-net, Graph Theory, TensorFlow, Keras.

Project – OPERATIONAL SYSTEM FOR AUTOMATIC CEPHALOMETRIC ANALYSIS

The project consists of the construction of an efficient and effective operational system for therecognition of anatomical points on a 2D cephalometric radio. This operation is essential for a goodpreparation of an orthodontist’s work. Among the multitude of existing points, we will set thepriorities according to the importance and the discriminating aspect of the point, facilitating itsautomatic detection. Supporting this first detection - which will be done using deep machinelearning - Bayesian models and graphs are to be considered to consolidate the reconstruction ofthe entire layout. If performance requires it, a 3D virtual model could be built and used to makethe approach more robust.

Context of the internship:

KITVIEW develops innovative solutions based on image analysis (medical and natural) tocontinuously improving the ergonomics of its software, offering advanced features to its customersand partners, as well as targeting new markets in France and abroad.

Competencies (selection) requested to reinforce our R&D projects:

- Image Classification, Pattern recognition, Machine Learning (SVM, Random Forest, HMM ...), DeepLearning (CNN, U-net, GAN, MIL - Libraries: TensorFlow...);- Medical image analysis, conditional probabilities, graph theory;- Rapid prototyping of ergonomic, modern software interfaces.

Applicant profile:

- University Master or Engineering School student (last year of study) with computer science, imageanalysis and/or applied mathematics profile;- Interest, curiosity, learning capability and creativity are qualities we do appreciate;- Positive spirit, communication skills and ability to work in a team, if necessary;- Autonomy, dynamism and motivation to advance his/her own part of the project;- Excellent methodological and hands-on computer programming skills. Programming languages:Python, C ++. Libraries: TensorFlow, Keras, Open CV, CUDA;- Facility of understanding and manipulating mathematical models.