  Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death
worldwide. Whole-slide imaging which is a method of scanning glass slides have
been employed for diagnosis of HCC. Using high resolution Whole-slide images is
infeasible for Convolutional Neural Network applications. Hence tiling the
Whole-slide images is a common methodology for assigning Convolutional Neural
Networks for classification and segmentation. Determination of the tile size
affects the performance of the algorithms since small field of view can not
capture the information on a larger scale and large field of view can not
capture the information on a cellular scale. In this work, the effect of tile
size on performance for classification problem is analysed. In addition, Multi
Field of View CNN is assigned for taking advantage of the information provided
by different tile sizes and Attention CNN is assigned for giving the capability
of voting most contributing tile size. It is found that employing more than one
tile size significantly increases the performance of the classification by
3.97% and both algorithms are found successful over the algorithm which uses
only one tile size.

Computer Vision and Pattern Recognition;Machine Learning