SAGE: Sequential Attribute Generator for Analyzing Glioblastomas Using Limited Dataset

While deep learning approaches have shown remarkable performance in many imaging tasks, most of these methods rely on the availability of large quantities of data. Medical imaging data, however, are scarce and fragmented. Generative Adversarial Networks (GANs) have recently been very effective in handling such datasets by generating more data. If the datasets are very small, however, GANs cannot learn the data distribution properly, resulting in less diverse or low-quality results. One such limited dataset is that for the concurrent gain of 19/20 chromosomes (19/20 co-gain), a mutation with positive prognostic value in Glioblastomas (GBM). In this paper, imaging biomarkers are detected for the mutation to streamline the extensive and invasive prognosis pipeline. Since this mutation is relatively rare, i.e. small dataset, a novel generative framework - the Sequential Attribute GEnerator (SAGE), is proposed, that generates detailed tumor imaging features while learning from a limited dataset. Experiments show that not only does SAGE generate high quality tumors when compared to Progressively Growing GAN (PGGAN), Wasserstein GAN with Gradient Penalty (WGAN-GP) and Deep Convolutional-GAN (DC-GAN), but also captures the imaging biomarkers accurately.