Masked V-Net: an approach to brain tumor segmentation
2017
This paper introduces Masked V-Net architecture, a variant
of the recently introduced V-Net[13] that reformulates the residual connections and uses a ROI mask to constrain the network to train only
on relevant voxels. This architecture allows dense training on problems
with highly skewed class distributions by performing data sampling on
the output instead of in the input. We use Masked V-Net in the context
of brain tumor segmentation and report results on the BraTS2017
Training and Validation sets.
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