ShearletBased Techniques for Histological Image Classification In this paper, we propose a new feature representation for automatic classification of different tissue types in histopathological images. Our proposed approach is to combine different sets of features into a novel texture feature space. We compute all features in the Complex Shearlet domain, where we have not only examined the magnitude coefficients, but we also incorporate the relative phase (RP) coefficients to form the feature representation. Our Shearlet feature space consists of Haralick texture features, segmentationbased Fractal Texture Analysis, Local Binary patterns, and Local Oriented Statistic Information Booster. These descriptors are also used jointly to increase the classification accuracy of histopathological images when used with standard classifier. We demonstrate the effectiveness of our novel feature space with a support vector machine (SVM). The proposed features provide high classification performance competing with stateoftheart systems equally on four publicly available datasets: WarwickQU, Epistroma, BreaKHis, multiclass Kather.

Texturebased Deep Learning for Effective Histopathological Cancer Image Classification Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques, since manual examination and diagnosis with WSIs are time and costconsuming. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. However, despite the success of the development, there are still opportunities for further enhancements. In this paper, we propose a novel cancer texturebased deep neural network (CATNet) that learns scalable morphological features from histopathological WSIs. The innovation of CATNet is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) improving predictive performance while reducing model complexity. Moreover, CATNet can provide discriminative morphological (texture) patterns formed on cancerous regions of histopathological images comparing to normal regions. We elucidated how our proposed method, CATNet, captures morphological patterns of interest in hierarchical levels in the model. The proposed method outperformed the current stateoftheart benchmark methods on accuracy, precision, recall, and F1 score.