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Latent Representation Learning for Alzheimer’s Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data
- Source :
- IEEE Trans Med Imaging
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- The fusion of complementary information contained in multi-modality data [ e.g. , magnetic resonance imaging (MRI), positron emission tomography (PET), and genetic data] has advanced the progress of automated Alzheimer’s disease (AD) diagnosis. However, multi-modality based AD diagnostic models are often hindered by the missing data, i.e. , not all the subjects have complete multi-modality data. One simple solution used by many previous studies is to discard samples with missing modalities. However, this significantly reduces the number of training samples, thus leading to a sub-optimal classification model. Furthermore, when building the classification model, most existing methods simply concatenate features from different modalities into a single feature vector without considering their underlying associations. As features from different modalities are often closely related ( e.g. , MRI and PET features are extracted from the same brain region), utilizing their inter-modality associations may improve the robustness of the diagnostic model. To this end, we propose a novel latent representation learning method for multi-modality based AD diagnosis. Specifically, we use all the available samples (including samples with incomplete modality data) to learn a latent representation space. Within this space, we not only use samples with complete multi-modality data to learn a common latent representation, but also use samples with incomplete multi-modality data to learn independent modality-specific latent representations. We then project the latent representations to the label space for AD diagnosis. We perform experiments using 737 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and the experimental results verify the effectiveness of our proposed method.
- Subjects :
- Male
Databases, Factual
genetic structures
Computer science
Feature vector
Feature extraction
Neuroimaging
Multimodal Imaging
Polymorphism, Single Nucleotide
Article
030218 nuclear medicine & medical imaging
Machine Learning
03 medical and health sciences
0302 clinical medicine
Alzheimer Disease
Robustness (computer science)
otorhinolaryngologic diseases
medicine
Humans
Diagnosis, Computer-Assisted
Electrical and Electronic Engineering
Genetic Association Studies
Aged
Aged, 80 and over
Radiological and Ultrasound Technology
medicine.diagnostic_test
business.industry
Brain
Pattern recognition
Missing data
Computer Science Applications
Positron emission tomography
Female
Artificial intelligence
business
Feature learning
Algorithms
psychological phenomena and processes
Software
Subjects
Details
- ISSN :
- 1558254X and 02780062
- Volume :
- 38
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Medical Imaging
- Accession number :
- edsair.doi.dedup.....b08c85772416e42b181a3d2c86a6b121