Neuroimaging Based Survival Time Prediction of GBM Patients Using CNNs from Small Data
| Autor: | Dmitry B. Goldgof, Kaoutar Ben Ahmed, Lawrence O. Hall, Renhao Liu, Robert A. Gatenby |
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| Rok vydání: | 2019 |
| Předmět: |
Small data
Computer science business.industry Feature extraction Pattern recognition medicine.disease Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Histogram of oriented gradients Neuroimaging medicine Segmentation Artificial intelligence business Classifier (UML) 030217 neurology & neurosurgery Glioblastoma |
| Zdroj: | SMC |
| DOI: | 10.1109/smc.2019.8913929 |
| Popis: | Here we investigate the application of convolutional neural networks (CNNs) to predict the survival time of patients with Glioblastoma Multiforme (GBM) brain tumor. Our dataset consists of T1-weighted high-resolution MRI images of just 68 GBM patients. We compare two analytic methods for predicting survival time. The first consists of training a small convolutional neural network (CNN) and the second uses extracted deep features from a pre-trained CNN. Our method is completely automated, except for tumor region segmentation. In addition, we utilize a snapshot ensemble approach to boost test accuracy when dealing with limited availability of medical images for CNN training purposes. Our approach achieves an accuracy of 72.06% using a trained small network and 66.18% using a pre-trained deep CNN. Our results compare favorably with the accuracy of 54.41% using histogram of oriented gradients (HOG) features and a non-neural network classifier. |
| Databáze: | OpenAIRE |
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