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3D Multi-Scale Conv...
3D Multi-Scale Convolutional Networks for Glioma Grading Using MR Images
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- Ge, Chenjie, 1991 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Qu, Qixun (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Gu, Irene Yu-Hua, 1953 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Jakola, Asgeir (författare)
- Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
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(creator_code:org_t)
- ISBN 9781479970612
- 2018
- 2018
- Engelska.
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Ingår i: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. - 9781479970612 ; , s. 141-145
- Relaterad länk:
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https://research.cha...
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https://research.cha...
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https://doi.org/10.1...
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https://research.cha...
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Abstract
Ämnesord
Stäng
- 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%).
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Neurologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Neurology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- MRIs
- Deep learning
- brain tumor classification
- Multi-scale features
- high/low grade glioma
- 3D CNN
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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