  This paper addresses the problem of liver cancer segmentation in Whole Slide
Image (WSI). We propose a multi-scale image processing method based on
automatic end-to-end deep neural network algorithm for segmentation of cancer
area. A seven-levels gaussian pyramid representation of the histopathological
image was built to provide the texture information in different scales. In this
work, several neural architectures were compared using the original image level
for the training procedure. The proposed method is based on U-Net applied to
seven levels of various resolutions (pyramidal subsumpling). The predictions in
different levels are combined through a voting mechanism. The final
segmentation result is generated at the original image level. Partial color
normalization and weighted overlapping method were applied in preprocessing and
prediction separately. The results show the effectiveness of the proposed
multi-scales approach achieving better scores compared to the state-of-the-art.
