Chapter title |
Structured Sparse Kernel Learning for Imaging Genetics Based Alzheimer’s Disease Diagnosis
|
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Chapter number | 9 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
|
Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-46723-8_9 |
Pubmed ID | |
Book ISBNs |
978-3-31-946722-1, 978-3-31-946723-8, 978-3-31-946722-1, 978-3-31-946723-8
|
Authors |
Peng, Jailin, An, Le, Zhu, Xiaofeng, Jin, Yan, Shen, Dinggang, Jailin Peng, Le An, Xiaofeng Zhu, Yan Jin, Dinggang Shen |
Abstract |
A kernel-learning based method is proposed to integrate multimodal imaging and genetic data for Alzheimer's disease (AD) diagnosis. To facilitate structured feature learning in kernel space, we represent each feature with a kernel and then group kernels according to modalities. In view of the highly redundant features within each modality and also the complementary information across modalities, we introduce a novel structured sparsity regularizer for feature selection and fusion, which is different from conventional lasso and group lasso based methods. Specifically, we enforce a penalty on kernel weights to simultaneously select features sparsely within each modality and densely combine different modalities. We have evaluated the proposed method using magnetic resonance imaging (MRI) and positron emission tomography (PET), and single-nucleotide polymorphism (SNP) data of subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The effectiveness of our method is demonstrated by both the clearly improved prediction accuracy and the discovered brain regions and SNPs relevant to AD. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 28 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 7 | 25% |
Student > Ph. D. Student | 5 | 18% |
Student > Master | 4 | 14% |
Student > Doctoral Student | 1 | 4% |
Student > Bachelor | 1 | 4% |
Other | 1 | 4% |
Unknown | 9 | 32% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 6 | 21% |
Neuroscience | 3 | 11% |
Computer Science | 3 | 11% |
Medicine and Dentistry | 2 | 7% |
Biochemistry, Genetics and Molecular Biology | 1 | 4% |
Other | 2 | 7% |
Unknown | 11 | 39% |