Capsule Networks’ Interpretability for Brain Tumor Classification Via Radiomics Analyses

Brain tumor, which is one of the deadliest cancers, can have several types, based on different characteristics of the tumor. Determining the exact category of this cancer is of significant importance, because it directly affects the treatment options and patient's survival. Although experts' judgment remains the gold standard for brain tumor classification, human-centered decision is time-consuming and error prone. Radiomics, referring to the extraction of features from medical images with the ultimate goal of cancer prediction/classification, can automatize the tumor classification task, having the promise of providing more accurate and time-effective decision. Hand-crafted Radiomics, which is the most common Radiomics analysis, needs a prior knowledge on the types of features to extract, which is not always available, leading to an increased surge of interest toward deep learning-based Radiomics. Although deep learning-based Radiomics does not need a prior knowledge, its practical application is limited by the low explainablity of the deep learning networks. In this work, the interpretability of Capsule networks, which have shown promising results in brain tumor classification, is explored. Outcomes show that the Radiomics features extracted by Capsule networks can not only distinguish between the tumor types, but also show considerable correlation with hand-crafted features, which are more acceptable and reliable from a physician's point of view.