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Multi-Stream Multi-...
Multi-Stream Multi-Scale Deep Convolutional Networks for Alzheimer's Disease Detection 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 Store (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi,Institute of Neuroscience and Physiology
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(creator_code:org_t)
- Elsevier BV, 2019
- 2019
- Engelska.
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 350, s. 60-69
- Relaterad länk:
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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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https://gup.ub.gu.se...
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Abstract
Ämnesord
Stäng
- This paper addresses the issue of Alzheimer's disease (AD) detection from Magnetic Resonance Images (MRIs). Existing AD detection methods rely on global feature learning from the whole brain scans, while depending on the tissue types, AD related features in dierent tissue regions, e.g. grey matter (GM), white matter (WM), and cerebrospinal uid (CSF), show different characteristics. In this paper, we propose a deep learning method for multi-scale feature learning based on segmented tissue areas. A novel deep 3D multi-scale convolutional network scheme is proposed to generate multi-resolution features for AD detection. The proposed scheme employs several parallel 3D multi-scale convolutional networks, each applying to individual tissue regions (GM, WM and CSF) followed by feature fusions. The proposed fusion is applied in two separate levels: the rst level fusion is applied on different scales within the same tissue region, and the second level is on dierent tissue regions. To further reduce the dimensions of features and mitigate overtting, a feature boosting and dimension reduction method, XGBoost, is utilized before the classication. The proposed deep learning scheme has been tested on a moderate open dataset of ADNI (1198 scans from 337 subjects), with excellent test performance on randomly partitioned datasets (best 99.67%, average 98.29%), and good test performance on subject-separated partitioned datasets (best 94.74%, average 89.51%). Comparisons with state-of-the-art methods are also included.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Neurologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Neurology (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Neurosciences (hsv//eng)
Nyckelord
- deep learning
- deep convolutional networks
- tissue region
- multi-scale feature learning
- feature boosting and dimension reduction
- Alzheimer's disease detection
- MR images
- feature fusion
- Alzheimer's disease detection
- MR images
- Deep learning
- Deep convolutional networks
- Multi-scale
- classification
- Computer Science
- OCESSING (ICASSP)IEEE International Conference on Acoustics
- Speech
- and Signal Processing
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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