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 availability of large quantities of data. Medical image data, however, is 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 and 20 chromosomes (19/20 co-gain), a mutation with positive prognostic value in Glioblastomas (GBM). In this paper, we detect imaging biomarkers for the mutation to streamline the extensive and invasive prognosis pipeline. Since this mutation is relatively rare, i.e. small dataset, we propose a novel generative framework - the Sequential Attribute GEnerator (SAGE), 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 standard Deep Convolutional GAN (DC-GAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP), it also captures the imaging biomarkers accurately

Augmenting Subjective Assessments with Objective Metrics for Neuromuscular Disorders

Spasticity is a debilitating neuro-muscular disorder which is characterized by involuntary muscle movements and stretch reflexes. There is a large population of the world who suffers with spasticity due to various diseases like Cerebral Palsy, Multiple Sclerosis, Spinal Cord Injury, Traumatic Brain Injury etc. The diagnosis standards of spasticity for treatment prescription are highly subjective, most of them either heavily based on the clinicians’ “feel”, on voluntary movements by patients or mounting sensors on patients with no defined correlation to the assessment standards. Hence, they have high inter- and intra-rater variability. Spasticity is diagnosed every few weeks whereas extent of spasticity can vary more frequently. Thus, a subjective assessment which does not account for the dynamic nature of spasticity is not a good measure for treatment. Moreover, the account of patients and their family affect the treatment as well. Treatment effectiveness and costs can vary highly based on inaccurate assessment. This calls for a dire need of an objective, consistent and repeatable scale for spasticity assessment. For this purpose, we have developed an instrumented glove in hopes that it will give such an assessment. The research of this Thesis describes the development of this glove, the algorithms to obtain an assessment measure and techniques to validate said glove. We also intend to make this glove so it instruments the clinicians or raters rather than the patients. This comes out of a consideration of both convenience to the patients and their finances