Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.
Peter ChangJack GrinbandBrent D WeinbergMichelle D BardisM KhyG CadenaMin-Ying Lydia SuSoonmee ChaChristopher G FilippiDaniela A BotaPierre BaldiLaila M PoissonRajan JainDaniel S ChowPublished in: AJNR. American journal of neuroradiology (2018)
Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training.
Keyphrases
- deep learning
- high grade
- convolutional neural network
- machine learning
- neural network
- artificial intelligence
- low grade
- high resolution
- endothelial cells
- genome wide
- papillary thyroid
- copy number
- big data
- squamous cell carcinoma
- magnetic resonance imaging
- squamous cell
- computed tomography
- fluorescence imaging
- induced pluripotent stem cells
- pluripotent stem cells