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Abstract #3490

Simulation of brain deficits on MRI: A novel approach of ‘ground truth’ generation for machine learning

Kattie Sepehri1, Xiaowei Song2, Ryan Proulx3, Sujoy Ghosh Hajra4, Brennen Dobberthien5, Careesa Liu6, Ryan D'Arcy7, Don Murray3, and Andra Krauze5
1UBC, Vancouver, BC, Canada, 2Surrey Memorial Hospital, Vancouver, BC, Canada, 3Safe Software, Vancouver, BC, Canada, 4National Research Council, Vancouver, BC, Canada, 5BC Cancer, Vancouver, BC, Canada, 6Baycrest Health Sciences Centre, Vancouver, BC, Canada, 7HealthTech Connex, Vancouver, BC, Canada

Robust machine learning algorithms for tumor identification require ground truth data sets. Ground truth data sets require expert input, are difficult and inefficient to produce. Feature Manipulation Engine (FME) allows for specific and complex data manipulation. We have created an FME workflow to produce simulated tumors that resemble realistic gliomas as rated by experts.

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