PhD Position in Deep Learning for Clinical Decision Support in Brain Imaging: Trustworthy Validation and Benchmarking

il y a 6 jours


Paris, Île-de-France INRIA Temps plein

Context and Advantages of the Position

You will work within the ARAMIS lab, which is part of Inria, the French National Institute for Research in Computer Science and Applied Mathematics, dedicated to developing new machine learning and statistical approaches for analyzing large neuroimaging and clinical data sets.

This PhD is funded as part of the Paris Artificial Intelligence Research Institute (PRAIRIE).

Research Mission

Deep learning-based analysis of brain imaging data holds great promises for assisting clinical decision in brain disorders (e.g. Alzheimer's disease, Parkinson's disease...). However, the real medical impact in terms of translation to patient care has been so far limited. Experimental results presented in research papers are most often inadequate for answering two key questions for clinical translation: i) do we have solid guarantees on the performance of the proposed approach?; ii) among different deep learning approaches, do we have strong evidence to select the one which performs best?

Our team has been a pioneer and has produced highly-cited work on reproducible and trustworthy evaluation of machine learning for computer-aided diagnosis of Alzheimer's disease. We have unveiled biased validation procedures, proposed frameworks to avoid them, performed large-scale benchmarks, and created an Open Source software platform for reproducible deep learning in brain imaging, ClinicaDL.

Key Activities

  • Enrich the framework with more advanced deep learning models, more tasks, and more datasets.
  • Better account for specificities of brain imaging (multiple acquisitions over time, multiple scanners, multiple hospitals, multiple datasets, multiple disorders).
  • Propose an adequate inferential statistics framework for both model validation and model comparison.
  • Perform benchmarking experiments across deep learning (and also standard machine learning) models, tasks, diseases, and datasets to create a new standard for the community.
  • Demonstrate the importance of accounting for brain imaging specificities when evaluating models.
  • Implement the approaches in open-source software, in particular ClinicaDL.

Requirements

  • Excellent background in machine learning.
  • Excellent programming skills in Python.
  • Strong interest for experimental aspects of machine learning.
  • Good background in statistics.
  • Knowledge of the specificities of computer vision and medical image processing would be a plus.
  • Good writing skills.
  • Good relational and communication skills.
  • Ability to collaborate efficiently with other team members.

Benefits

  • Subsidized meals.
  • Partial reimbursement of public transport costs.
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.).
  • Possibility of teleworking and flexible organization of working hours (after 12 months of employment).
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.).
  • Social, cultural, and sports events and activities.
  • Access to vocational training.
  • Social security coverage.


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