A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19
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 withdoi:10.24926/548719.056 fatcat:qaprbndepja3tbs3ot2gyhdsrm