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Abstract #1887

Synthetic CMB generation for Training classifiers on QSM images

Saba Momeni1,2, Amir Fazlollahi1, Pierrick Bourgeat1, Paul Yate3, Nawaf Yassi4, Patricia Desmond5, Yongsheng Gao2, Alan Wee Chung Liew6, and Olivier Salvado7
1CSIRO Health and Biosecurity, Brisbane, Australia, 2Griffith University, Brisbane, Australia, 3Department of Nuclear Medicine and Centre for PET, Brisbane, Australia, 4University of Melbourne, Parkville, Australia, Melbourne, Australia, 5Austin Health Heidelberg, Australia, Melbourne, Australia, 6Griffith University, Gold coast, Australia, 7Data61, Brisbane, Australia

The lack of clinical dataset with enough examples of rare lesions challenges supervised machine learning methods. Here we propose to generate synthetic lesions for training a classifier to identify microbleeds from MRI QSM. We show that the performance of the classifier is improved compare to standard data augmentation using actual data alone. Our synthetic dataset can have unlimited size allowing to perform validation experiment, while keeping the actual data for testing. Moreover, many aspects of the data can be investigated, which would not be possible when using actual lesions: lesions can be synthetize on any location, size, shape, and intensity.

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