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Abstract #0899

Brain Tumor Segmentation and Uncertainty Quantification Using Monte Carlo dropout sampling

Joohyun Lee1, Woojin Jung1, and Jongho Lee1

1Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of

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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