  Correct treatment of urothelial carcinoma patients is dependent on accurate
grading and staging of the cancer tumour. This is determined manually by a
pathologist by examining the histological whole-slide images (WSI). The large
size of these images makes this a time-consuming and challenging task. The WSI
contain a variety of tissue types, and a method for defining diagnostic
relevant regions would have several advantages for visualization as well as
further input to automated diagnosis systems. We propose an automatic
multiscale method for classification of tiles from WSI of urothelial carcinoma
patients into six classes. Three architectures based on convolutional neural
network (CNN) were tested: MONO-CNN (400x), DI-CNN (100x/400x) and TRI-CNN
(25x/100x/400x). The preliminary results show that the two multiscale models
performed significantly better than the mono-scale model, achieving an F1-score
of 0.986, substantiating that utilising multiple scales in the model aids the
classification accuracy.
