Immunohistology fluorescence image analysis is an important method for cancer diagnosis. With the widespread application of convolutional neural networks in computer vision, segmentation of images of cancer cells has become an important topic in medical image analysis. Although there are many publications describing the success in application of deep learning models for segmentation of different kind of histology images, the universal algorithm is still not developed. The image preprocessing consisting in splitting images in smaller parts and normalization is important in deep learning especially when the training set is of a limited size. In this study, we compared several approaches to create the training set of a sufficient size while having a limited number of labeled whole slide immunohistology images of cancer cells. Also, we explored different normalization methods
