Name
Abstract | ||
|---|---|---|
This paper addresses issues of grading brain tumor, glioma, from Magnetic Resonance Images (MRIs). Although feature pyramid is shown to be useful to extract multi -scale features for object recognition, it is rarely explored in MRI images for glioma classification/grading. For glioma grading, existing deep learning methods often use convolutional neural networks (CNN s) to extract single-scale features without considering that the scales of brain tumor features vary depending on structure/shape, size, tissue smoothness, and locations. In this paper, we propose to incorporate the multi-scale feature learning into a deep convolutional network architecture, which extracts multi -scale semantic as well as fine features for glioma tumor grading. The main contributions of the paper are: (a) propose a novel 3D multi-scale convolutional network architecture for the dedicated task of glioma grading; (b) propose a novel feature fusion scheme that further refines multi -scale features generated from multi-scale convolutional layers; (c) propose a saliency -aware strategy to enhance tumor regions of MRIs. Experiments were conducted on an open dataset for classifying high/low grade gliomas. Performance on the test set using the proposed scheme has shown good results (with accuracy of 89.47%). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICIP.2018.8451682 | 2018 25th IEEE International Conference on Image Processing (ICIP) |
Keywords | Field | DocType |
Multi-scale features,3D CNN,brain tumor classification,high/low grade glioma,MRIs | Computer vision,Pattern recognition,Salience (neuroscience),Convolutional neural network,Computer science,Network architecture,Feature extraction,Artificial intelligence,Deep learning,Feature learning,Cognitive neuroscience of visual object recognition,Test set | Conference |
ISSN | ISBN | Citations |
1522-4880 | 978-1-4799-7062-9 | 1 |
PageRank | References | Authors |
0.43 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenjie Ge | 1 | 2 | 1.46 |
| Qixun Qu | 2 | 2 | 0.79 |
| Irene Yu-Hua Gu | 3 | 613 | 35.06 |
| A S Jakola | 4 | 12 | 4.15 |