3D MULTI-SCALE CONVOLUTIONAL NETWORKS FOR GLIOMA GRADING USING MR IMAGES
Paper i proceeding, 2018

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 (CNNs) 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%).

brain tumor classification

Multi-scale features

3D CNN

Deep learning

high/low grade glioma

MRIs

Författare

Chenjie Ge

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Qixun Qu

Chalmers tekniska högskola

Irene Yu-Hua Gu

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Asgeir Jakola

Sahlgrenska universitetssjukhuset

5 pages-

2018 IEEE International Conference on Image Processing (ICIP'18)
Athens, Greece, ,

Ämneskategorier

Datorteknik

Medicinteknik

Neurologi

Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)

Cancer och onkologi

Medicinsk bildbehandling

Drivkrafter

Hållbar utveckling

Styrkeområden

Livsvetenskaper och teknik