It is often difficult to share denominated data between different organisations and researchers due to ethical constraints related to respondent's confidentiality. This is a frequent reality in healthcare, given the inherent sensitivity of the data involved. One option in this case is to not share the data directly, but rather to provide access to it via a tool that controls the risk of disclosure of the queries made and allows only those it considers safe. DataSHIELD is such a tool that has been proposed to protect the confidentiality of a dataset, and which is used via the statistical software R. It also allows statistical analyses to be carried out on several datasets hosted in different locations, always ensuring the confidentiality of the respondents. In this project, we are interested in the confidentiality guarantees provided by the software, and in its limitations. In particular, we study the potential uses of the software for advanced statistical analyses, such meta-analyses and the use of neural networks.

Treatment of cancer with radiation is a proven technique used worldwide. One of the ways to treat prostate cancer is by using brachytherapy either alone or as a boost. At the moment, the techniques used depend on the experience of the treatment team and researchers are trying to overcome this problem.
In our case, the technology considered to address this problem is deep learning. Therefore, the aim of this project is to use deep learning to develop tools for planning in high dose rate brachytherapy for the treatment of prostate cancer.
Four different phases are initially targeted. The first consists of a classification of treatment plans. The second is a “reinforce learning” approach to help optimize treatment plans, by modifying the optimization objectives in order to consider each patient in a unique way. The third is dose map prediction based on patient anatomy. The fourth is the generation of treatment plans; from the patient's anatomy or from a dose map to find an adequate treatment plan.
The proposed work is a new approach that will ultimately help with the planning of high dose rate brachytherapy treatments for prostate cancer.
 

Recent breakthroughs in medicine have shown a link between cerebrovascular pathologies and the risk of developing mild or major cognitive disorders of the like of Alzheimer’s disease (AD). The project will thus consist in developing a tool for the characterisation of the cerebrovascular system.
Indeed, the measurement of arteries and veins (diameter, density, etc.) in every brain region will
provide a better understanding of the vascular health changes throughout the aging process and if these changes are linked to two markers heavily correlated with neurocognitive disorders: cerebral tissue atrophy and cerebrovascular lesions (mainly hyperintensities in white matter and cerebral microbleeds). Thus, the cerebrovascular system for cognitively healthy participants, patients with mild cognitive disorder and patients with AD will be characterized to better our understanding of cognitive health’s link with cerebrovascular health. 
To summarize, the project is an exploration of the relation between the cerebrovascular system and the AD in order to comprehend the interactions involved and help with the diagnosis of the disease.
 

Interpretability of Artificial Intelligence, that is the capacity of an expert to understand why a prediction is made, is of great importance in health analysis. Firstly, because it matters to understand why a decision is made by an algorithm when it has such impact on a person’s life. Moreover, in research, interpretable algorithms are useful because they often unveil new investigation path. 

This study aims to combine two supervised machine learning algorithms to optimize both interpretability and performance, for instance, with mathematical logic tools. This new algorithm intends to help better predictions by lightly increasing model complexity while preserving high interpretability. 

This algorithm is developed to analyze fat data, which are data with a lot of characteristics (features) but with few samples (observations). This type of data is recurrent in health data, mainly in genomics, metagenomics and metabolomics data, which are all state of the art in medical analysis. More precisely, we are interested in problems such as antibiotic resistance or long corona virus disease (COVID-19).