FOURNIER Laure

Medical imaging

laure.fournier [at] parisdescartes.fr

Laure Fournier

Short bio

MD, PhD, Professor of Radiology, Hôpital Européen Georges Pompidou, Université de Paris. Ecole Normale Supérieure (1991-1995), Research Fellow, UCSF, San Francisco, CA, USA (2001-2003). Responsible for the organisation of courses in Artificial Intelligence in Radiology for radiology residents, Member of the working group on Artificial Intelligence for the CERF – SFR, Member of the Scientific Committee of the DRIM France IA database. Grants over 650 k€ on radiomics and AI in medical imaging projects.

Topics of interest

Medical imaging, machine learning, radiomics, computer vision

Project in Prairie

Our project will focus on three approaches: 1) methodological developments on radiomics, i.e. high throughput extraction and selection of features from medical images using strategies including feature engineering, and deep learning and neural networks; 2) constitution of real-time prospective databases to obtain exploitable training and test data for the applications developed in Prairie; 3) integration of multimodality and multiparametric data stemming from multi-scale imaging going from microscopy to anatomical (radiology) and functional imaging.

Quote

Developments in computer vision need to translate into benefits for patients by transferring tools and applications developed for non-medical images to microscopic and macrsocopic medical imaging. The integration of this very diverse data to obtain a comprehensive view of a patient and his disease is a challenge which we must undertake in Prairie. The relative low numbers of patient data compared to the large number of features and parameters describing the patient and his disease, and the time-consuming annotation, remain important challenges and will require new tools which can train and learn on datasets with a weaker level of human supervision.

DURRLEMAN Stanley

Statistical learning, imaging, neurosciences

stanley.durrleman [at] inria.fr / Twitter: @SDurrleman

Stanley Durrleman

Short bio

Stanley Durrleman is heading the joint Inria / ICM ARAMIS research team at the Brain Institute (ICM) in Paris. He is the founding director of the ICM centre for neuroinformatics. His research has earned him several international awards, including a European Research Council (ERC) award in 2015.

Topics of interest

Geometry and learning, neuroimaging, brain disorders, disease modeling, digital twins

Project in Prairie

We will develop novel statistical and computational approaches at the cross-roads of geometry and learning. These approaches built on generic principles will allow the exploitation of a large variety of structured and unstructured data such as clinical data, structural and functional imaging. These methods are well suited to deal with repeated data from the same patients over time (i.e. longitudinal data), so that they can be used to synthetize digital models of disease progression. The personalization of such models to new patient data will enable the implementation and evaluation of personalized therapeutic strategies.

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Better understanding the brain and its disorders is probably the most fascinating scientific and medical challenge of this century. The repeated failures to find efficient treatments against most neurological diseases require to explore radically different approaches. At the core of one of the major European hospital and neuroscience research institute, we develop novel data-driven approaches to exploit large databases of neuroscience data including imaging, clinical, physiological and genomics data. We simulate the progression of neurodegenerative diseases. We design and evaluate decision support systems informed by personalized prediction of disease progression. Our research contributes therefore to the emergence of a precision medicine in neurology.

COLLIOT Olivier

Machine learning for medical imaging

olivier.colliot [at] upmc.fr

Olivier Colliot

Short bio

Olivier Colliot is Research Director at CNRS and the founding head of the ARAMIS Lab, a joint team between CNRS, Inria, Inserm and Sorbonne University at the Brain and Spine Institute (ICM). Founded in 2012, ARAMIS gathers about 35 people dedicated to data science and AI for studying diseases of the brain. Prior to that, Olivier Colliot obtained a PhD in Computer Science from Telecom ParisTech in 2003, was a postdoctoral fellow at McGill University from 2003 to 2005 and joined the CNRS as a permanent researcher in 2006. He is the author of over 80 journal papers and is a member of the editorial board of Medical Image Analysis, one of the two leading journals of the field.

Topics of interest

Machine learning, computer vision, medical imaging, multimodal medical data (imaging, genomics)

Project in Prairie

His research will be dedicated to interpretable machine learning for neuroradiology. The main research threads are: i) the design of approaches for more interpretable AI, ii) generic computer-aided diagnosis systems, iii) integration of multimodal data and iv) methodologies for reproducible research. Medical applications will be devoted to neurological diseases, in particular using large-scale clinical routine data.

Quote

Brain imaging is a domain in which AI hold major promises. However, current systems are not interpretable and too narrow. These are major barriers to their adoption by clinicians. I firmly believe that fundamental research advances are needed to make systems more interpretable and generic. I hope that these ultimately lead to better diagnosis and care of patients. I am really excited to conduct this project within PRAIRIE, which will open new fruitful collaborations with academic and industrial partners. I also believe we have a major role to play in training in AI the next generation of engineers but also clinicians, that our country needs.

BURGOS Ninon

Medical Image Computing

ninon.burgos [at] icm-institute.org

Ninon Burgos

Short bio

Ninon Burgos is a CNRS researcher in the ARAMIS Lab (Brain and Spine Institute – ICM). She received three travel awards to attend major international conferences (2013, 2015, 2016), a postdoctoral fellowship from Campus France and the Marie Curie Actions (2016), a prize for the best publication in Physica Medica (2017), and the Cor Baayen Young Researcher Award 2019.

Topics of interest

Medical imaging, computer-aided diagnosis, machine learning

Project in Prairie

Ninon Burgos will focus on the individual analysis of medical images to improve differential diagnosis and strengthen personalised medicine. This project will involve developing advanced computational representations of multimodal imaging data and building flexible decision support systems. The framework will be applied to brain images to assist in the diagnosis of neurological diseases.

Quote

Neuroimaging offers an unmatched description of the brain, which explains
its crucial role in the understanding and diagnosis of neurological disorders. There is a critical need to develop new methods that can improve the characterisation of each patient’s pathology, and to build decision support systems more sensitive and easier to interpret.