SAGE: Sequential Attribute Generator for Analyzing Glioblastomas using Limited Dataset
| Autor: | Brent D. Weinberg, Taejin L. Min, Padmaja Jonnalagedda, Shiv Bhanu, Bir Bhanu, Jason W. Allen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Tumor imaging Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Deep learning Pipeline (computing) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition Pattern recognition 010501 environmental sciences Electrical Engineering and Systems Science - Image and Video Processing 01 natural sciences Visualization 03 medical and health sciences 0302 clinical medicine Mutation (genetic algorithm) FOS: Electrical engineering electronic engineering information engineering Artificial intelligence Medical imaging data business 030217 neurology & neurosurgery 0105 earth and related environmental sciences Generator (mathematics) |
| Zdroj: | ICPR |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|