Abstract
Structural magnetic resonance imaging (sMRI) has been widely used in computer-aided diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Based on sMRI data, anatomical-landmark-based deep learning has been recently proposed for AD and MCI diagnosis. These methods usually first locate informative anatomical landmarks in brain sMR images, and then integrate both feature learning and classification training into a unified framework. This chapter presents the latest anatomical-landmark-based deep learning approaches for automatic diagnosis of AD and MCI. Specifically, an automatic landmark discovery method is first introduced to identify discriminative regions in brain sMR images. Then, a landmark-based deep learning framework is presented for AD/MCI classification, by jointly performing feature extraction and classifier training. Experimental results on three public databases demonstrate that the proposed framework boosts the disease diagnosis performance, compared with several state-of-the-art sMRI-based methods.
| Original language | English |
|---|---|
| Title of host publication | Intelligent Systems Reference Library |
| Publisher | Springer |
| Pages | 127-147 |
| Number of pages | 21 |
| DOIs | |
| Publication status | Published - 2020 Jan 1 |
| Externally published | Yes |
Publication series
| Name | Intelligent Systems Reference Library |
|---|---|
| Volume | 171 |
| ISSN (Print) | 1868-4394 |
| ISSN (Electronic) | 1868-4408 |
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ASJC Scopus subject areas
- Computer Science(all)
- Information Systems and Management
- Library and Information Sciences
Cite this
Anatomical-Landmark-Based Deep Learning for Alzheimer’s Disease Diagnosis with Structural Magnetic Resonance Imaging. / Liu, Mingxia; Lian, Chunfeng; Shen, Dinggang.
Intelligent Systems Reference Library. Springer, 2020. p. 127-147 (Intelligent Systems Reference Library; Vol. 171).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Anatomical-Landmark-Based Deep Learning for Alzheimer’s Disease Diagnosis with Structural Magnetic Resonance Imaging
AU - Liu, Mingxia
AU - Lian, Chunfeng
AU - Shen, Dinggang
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Structural magnetic resonance imaging (sMRI) has been widely used in computer-aided diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Based on sMRI data, anatomical-landmark-based deep learning has been recently proposed for AD and MCI diagnosis. These methods usually first locate informative anatomical landmarks in brain sMR images, and then integrate both feature learning and classification training into a unified framework. This chapter presents the latest anatomical-landmark-based deep learning approaches for automatic diagnosis of AD and MCI. Specifically, an automatic landmark discovery method is first introduced to identify discriminative regions in brain sMR images. Then, a landmark-based deep learning framework is presented for AD/MCI classification, by jointly performing feature extraction and classifier training. Experimental results on three public databases demonstrate that the proposed framework boosts the disease diagnosis performance, compared with several state-of-the-art sMRI-based methods.
AB - Structural magnetic resonance imaging (sMRI) has been widely used in computer-aided diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Based on sMRI data, anatomical-landmark-based deep learning has been recently proposed for AD and MCI diagnosis. These methods usually first locate informative anatomical landmarks in brain sMR images, and then integrate both feature learning and classification training into a unified framework. This chapter presents the latest anatomical-landmark-based deep learning approaches for automatic diagnosis of AD and MCI. Specifically, an automatic landmark discovery method is first introduced to identify discriminative regions in brain sMR images. Then, a landmark-based deep learning framework is presented for AD/MCI classification, by jointly performing feature extraction and classifier training. Experimental results on three public databases demonstrate that the proposed framework boosts the disease diagnosis performance, compared with several state-of-the-art sMRI-based methods.
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U2 - 10.1007/978-3-030-32606-7_8
DO - 10.1007/978-3-030-32606-7_8
M3 - Chapter
AN - SCOPUS:85075530250
T3 - Intelligent Systems Reference Library
SP - 127
EP - 147
BT - Intelligent Systems Reference Library
PB - Springer
ER -