P250 - Feature Disentanglement to Aid Imaging Biomarker Characterization for Genetic Mutations
Padmaja Jonnalagedda, Brent Weinberg, Jason Allen, Bir Bhanu
Various mutations have been shown to correlate with prognosis of High-Grade Glioma (Glioblastoma). Overall prognostic assessment requires analysis of multiple modalities: imaging, molecular and clinical. To optimize this assessment pipeline, this paper develops the first deep learning-based system that uses MRI data to predict 19/20 co-gain, a mutation that indicates median survival. The paper addresses two key challenges when dealing with deep learning algorithms and medical data: lack of data and high data imbalance. We propose an unified approach that consists of a Feature Disentanglement (FeaD-GAN) technique for generating synthetic images to address these challenges, that projects features and re-samples from a pseudo-larger data distribution to generate synthetic images from very limited data. A thorough analysis is performed to (a) characterize aspects of visual manifestation of 19/20 co-gain that demonstrates the effectiveness of FeaD-GAN and (b) demonstrate that not only do the imaging biomarkers of 19/20 co-gain exist, but they are reproducible as well.
Poster Session #6 - 13:30 - 15:00 UTC-4 (Wednesday)