Authors
HE, XueJian
Shun Leung, Ping
Wang, Lu
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Issue Date
2019
Publisher
University of Minnesota Libraries Publishing
Type
Article
Abstract
An enhanced U-Net model with multi-scale inputs and deep supervision are adopted for Kidney tumor segmentation. Focal Tversky Loss is used to train the model, in order to improve the model performance of detecting small tumors. Progressive training is proposed for facilitating model converge. A simple postprocessing method is used to remove segmentation noises. The preliminary results indicate that the proposed model can segment the normal kidney with a satisfactory result; for the tumors with small sizes in low contrast or extreme sizes, there is still a room for improvement.
Identifiers
doi: 10.24926/548719.056