Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Medical images must be interpreted by medical professionals to determine the presence of disease. Like allx000D
humans, medical professionals have limited attention and make mistakes. Furthermore, modern imagingx000D
equipment produce larger and more complex data which the human observer struggles to analyze. Modernx000D
computer algorithms can identify more subtle patterns, can analyze larger datasets, learn from more examplesx000D
and are reproducible. Nevertheless we do not always know how to program computers for certain tasks andx000D
therefore rely on adaptive algorithms to "learn" to detect disease. To leverage the strength of expert humanx000D
observers and machine observers we require two-way collaboration between man and machine. In this projectx000D
we propose to adapt an existing medical image viewing system, in collaboration with its vendor (Hermesx000D
Medical Solutions), to enable computer aided diagnosis (where the machine helps the physician) andx000D
supervised machine learning (where the user provides corrective feedback to the learning machine).