SLATE ANTARES MIC MODELER SOFTWARE

It proposes first to revisit data integration approaches by taking into account an existing hierarchy in these data, and then to explicitly learn such a hierarchy from the data, taking into account both the knowledge gain brought by new descriptors and the costs related to the clinical context and data processing.The project focuses on applications in cardiac imaging, with retrospective exploration of existing heart failure studies from common imaging protocols (magnetic resonance and echocardiography), where efforts will be specifically dedicated to clinical transfer and interpretation, via the development of practical software tools to navigate through complex data and results.ДЎ. The project aims to revisit the representation of diseases from medical imaging, and thus improve the monitoring and risk stratification of both individuals and groups of patients.

MIC-MAC aims to substantially improve the analysis of medical images on these aspects. Data descriptors are numerous, can be high-dimensional and of heterogeneous types, and their combination is not trivial. Representation learning and in particular unsupervised learning enable knowledge discovery and risk stratification in medical databases, but need to solve key issues in terms of data integration. CE45 - MathГ©matiques et sciences du numГ©rique pour la biologie et la santГ© Modeling hIerarchy between Cardiac descriptors with MAChine learning вЂ“ MIC-MAC Modeling hIerarchy between Cardiac descriptors with MAChine learning

-slate -antares mic modeler