An Ensemble Approach for Histopathological Classification of Vulvar Cancer

Light microscopy of tissue slides is an important tool for analyzing human diseases including cancer. In this work, we focus on classifying patches from a immunohistochemically stained tissue microarray (TMA) of vulvar cancer. We propose a novel ensemble-based deep learning technique to classify patches of tissue as cancerous, stroma, both, or none. Our ensemble model consists of a pre-trained data-efficient image transformer (DeiT) module to extract features of patches followed by a transformer block and graph convolutional networks (GCN). Transformer blocks aid the sequential learning of the extracted features from DeiT while the graph convolutional network (GCN) extracts neighborhood information. Our approach combines both methods for classification. In the evaluation, we show that our approach outperforms state-of-the-art architectures for the addressed application. We also show that is applicable when only small amounts of labelled data are available.