Tissue Pattern Classification with CNN in Histological Images

Tissue pattern is an important factor in morphological evaluation of tissue samples. It can be decisive in disease discrimination or for establishing disease subtypes. Tissue architecture can be generally described by pathologist as classical, hypocellular or hypercellular. This article presents a study used to establish classification convolutional neural network model for tissue compactness assessment. The VGG16 network was trained to classify image patches to create reliable heatmaps. The application of image augmentation, class-specific sampling, and hyperparameter tuning was used to prevent overfitting and increase the accuracy of the model. Based on the current results it can be concluded that the differentiation between hypo- and hypercellular tissue (compactness) is possible with application of deep learning classification model VGG16. We hope to find correlation of features related to tissue compactness with subtypes of the analysed disease that would become diagnostic markers or prognostic factors.