  We propose a computationally efficient architecture that learns to segment
lesions from CT images of the liver. The proposed architecture uses bilinear
interpolation with sub-pixel convolution at the last layer to upscale the
course feature in bottle neck architecture. Since bilinear interpolation and
sub-pixel convolution do not have any learnable parameter, our overall model is
faster and occupies less memory footprint than the traditional U-net. We
evaluate our proposed architecture on the highly competitive dataset of 2017
Liver Tumor Segmentation (LiTS) Challenge. Our method achieves competitive
results while reducing the number of learnable parameters roughly by a factor
of 13.8 compared to the original UNet model.
