The identification of tissue regions within histopathological images represents a fundamental step for diagnosis, patient stratification and follow-up. However, the huge amount of image data made available by the ever improving whole-slide imaging devices gives rise to a bottleneck in manual, microscopy-based evaluation. Furthermore, manual procedures generally show a significant intra- and/or inter-observer variability. In this scenario the objective of this chapter is to investigate the effectiveness of image features from last-generation, pre-trained convolutional networks against variants of Local Binary Patterns for classifying tissue sub-regions into meaningful classes such as epithelium, stroma, lymphocytes and necrosis. Experimenting with seven datasets of histopathological images we show that both classes of methods can be quite effective for the task, but with a noticeable superiority of descriptors based on convolutional neural networks. In particular, we show that these can be seamlessly integrated with standard classifiers (e.g. Support Vector Machines) to attain overall discrimination accuracy between 95 and 99%.
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