Classification of Histology Images based on a Compact 3D Representation

Although the Convolutional Neural Networks (CNNs) have been widely adopted for the classification of histopathology images, one of the main drawbacks of CNNs is their inability to cope with gigapixel images. To deal with the high resolution of Whole Slide Image (WSI), many methods focus on patch processing which can result in improper representation if the patches are analyzed independently, losing the context information that is fundamental in digital pathology. In this study, the WSI is mapped into a compressed representation preserving the topological and morphological information relating to spatial correlations of neighboring patch features of the WSI. Such a representation is used to train a CNN to solve a classification task of breast histological images. The effectiveness of the suggested framework is demonstrated through experiments on Camelyon16 dataset. The results show the performance of our approach when three different ways to incorporate the spatial correlation in the tensor are used singly or in combination, highlighting that it is comparable with the state of the art.