Reference number : GC-2017-Q3-00510, amendment number : , agreement type : G, recipient legal name : Klein, Ran (University of Ottawa)|Klein, Ran (Université d’Ottawa), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Medical images must be interpreted by medical professionals to determine the presence of disease. Like all_x000D_
humans, medical professionals have limited attention and make mistakes. Furthermore, modern imaging_x000D_
equipment produce larger and more complex data which the human observer struggles to analyze. Modern_x000D_
computer algorithms can identify more subtle patterns, can analyze larger datasets, learn from more examples_x000D_
and are reproducible. Nevertheless we do not always know how to program computers for certain tasks and_x000D_
therefore rely on adaptive algorithms to "learn" to detect disease. To leverage the strength of expert human_x000D_
observers and machine observers we require two-way collaboration between man and machine. In this project_x000D_
we propose to adapt an existing medical image viewing system, in collaboration with its vendor (Hermes_x000D_
Medical Solutions), to enable computer aided diagnosis (where the machine helps the physician) and_x000D_
supervised machine learning (where the user provides corrective feedback to the learning machine)., program purpose french : Medical images must be interpreted by medical professionals to determine the presence of disease. Like all_x000D_
humans, medical professionals have limited attention and make mistakes. Furthermore, modern imaging_x000D_
equipment produce larger and more complex data which the human observer struggles to analyze. Modern_x000D_
computer algorithms can identify more subtle patterns, can analyze larger datasets, learn from more examples_x000D_
and are reproducible. Nevertheless we do not always know how to program computers for certain tasks and_x000D_
therefore rely on adaptive algorithms to "learn" to detect disease. To leverage the strength of expert human_x000D_
observers and machine observers we require two-way collaboration between man and machine. In this project_x000D_
we propose to adapt an existing medical image viewing system, in collaboration with its vendor (Hermes_x000D_
Medical Solutions), to enable computer aided diagnosis (where the machine helps the physician) and_x000D_
supervised machine learning (where the user provides corrective feedback to the learning machine)., agreement title english : Integration of Human and Machine Observers for the Interpretation of Medical Images, agreement title french : Integration of Human and Machine Observers for the Interpretation of Medical Images, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-11-08, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada