This PhD project aims at obtaining a general methodological and experimental framework for trustworthy and reproducible validation and benchmarking of deep learning methods in brain imaging and performing large-scale experiments.
Specific objectives are as follows:
– 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 so that they can benefit the entire scientific community
Master or engineering degree with a specialization in machine learning.
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.
You will work within the ARAMIS lab at the Paris Brain Institute, one of the world top research institutes for neurosciences. The institute is ideally located at the heart of the Pitié-Salpêtrière hospital, downtown Paris.
The ARAMIS lab, which is also part of Inria (the French National Institute for Research in Computer Science and Applied Mathematics), is dedicated to the development of new machine learning and statistical approaches for the analysis of large neuroimaging and clinical data sets.
