Deep learning has made tremendous progress in many areas but it is often regarded as a black box with uncertainty in outcome. Therefore, a more reliable method is necessary to be applied in a medical field. In this work, we designed a brain tumor segmentation network that provides uncertainty quantification using Monte Carlo dropout sampling. The proposed method resulted in considerable outcomes and also provided an option for selectively maximizing precision or recall using the uncertainty quantification.
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