Snabbfakta
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- Paris
Ansök senast: 2024-06-01
PhD Position F/ M Artificial intelligence tools for clinical data warehouses in neuroimaging (H/ F)
Contexte et atouts du poste
You will work within the ARAMIS Lab (
The PhD thesis will be co-directed by Ninon Burgos (Research Scientist, HDR) and Olivier Colliot (Research Director). The position is funded through the GALAN project, a large-scale national grant in collaboration between the ARAMIS Lab, the Lille Neurosciences and Cognition Research Team, the departments of neuroradiology of the Pitié-Salpêtrière hospital and of the CHU of Lille, and the teams in charge of the CDWs of AP-HP and CHU of Lille. You will be involved in these collaborations and interact with the different partners.
Mission confiée
In recent years, very large clinical data warehouses (CDW) have been created containing the medical data of millions of patients. The AP-HP (Assistance Publique-Hôpitaux de Paris) CDW brings together data from multiple hospitals in the Paris region, including clinical data, diagnoses, medical reports and medical imaging data. CDWs provide fantastic opportunities to revolutionize digital healthcare. However, harnessing CDWs for research raises major challenges, among which controlling for data quality, biases and dealing with the full range of possible disorders and medical conditions.
Our team is a pioneer on the topic of neuroimaging in CDWs. We have built the first automatic quality control system for T1-weighted brain MRI of CDWs [1,2], that we subsequently extended to FLAIR MRI [3]. We have demonstrated that AI models trained on research data failed to generalize to clinical routine data and that, when data quality is not adequately taken into account, this leads to the catastrophic phenomenon of “short-cut” learning where the AI model learns to recognize image quality in place of the radiological features of the disorders [4].
The general objective of this PhD thesis project is to develop AI-based tools to harness the full potential of neuroimaging data in CDWs and to demonstrate that they can be used to develop trustworthy and unbiased AI-assisted reading systems for neuroradiology. Specific objectives are:
The work will also include data management and preparation tasks, installation of code and dependencies in specific environments, and performance benchmarking. The methodological developments will be integrated into ClinicaDL (
This project is expected to have a major impact on several aspects. It will allow researchers to fully exploit the very rich but complex neuroimaging data in CDWs, potentially leading to major new discoveries in various fields such as a better understanding of the factors influencing healthy and pathological brain aging. The project will result in next generation AI-based diagnostic tools that are expected to generalize well and thus have a high potential for translation to the clinic.
Principales activités
Main activities:
researchwrite scientific paperspresent work at scientific conferencesprogrammingdata managagement and curationinteract with partners (clinicians, scientists, engineers)
Compétences
Avantages