Authors
Ma, Jun

Abstract
Kidney and kidney tumor segmentation are essential steps in kidney cancer surgery. In this paper, we focus on addressing hard cases and exploring the kidney tumor shape prior rather than developing new convolution neural network architectures. Specifically, we train additional tumor segmentation networks to bias the ensemble classifier to tumor. Moreover, we propose the compact loss function to constrain the shape of the tumor segmentation results. Experiments on KiTS challenge show that both hard mining and compact can improve the performance of U-Net baseline.

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