ArticleMachine learning in medical imaging. MLMI (Workshop)2017
Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-status Dementia Diagnosis.
Article in Machine learning in medical imaging. MLMI (Workshop), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
12 citing papers in PubMed, 30 citations in OpenAlex.
- Deep joint learning diagnosis of Alzheimer's disease based on multimodal feature fusion.BioData mining · 2024Article
- A review of brain imaging biomarker genomics in Alzheimer's disease: implementation and perspectives.Translational neurodegeneration · 2022Review
- A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data.International journal of environmental research and public health · 2021Article
- Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool for Dementia Severity Staging: Development and Validation of a Machine Learning Approach.Journal of medical Internet research · 2020Article
- Spatially-Constrained Fisher Representation for Brain Disease Identification With Incomplete Multi-Modal Neuroimages.IEEE transactions on medical imaging · 2020Article
- Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.Psychiatry research · 2020Review
- Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis.Human brain mapping · 2019Article
- Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3.Frontiers in neuroinformatics · 2019Article
- Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model.IEEE transactions on bio-medical engineering · 2019Article
- Multi-modal Neuroimaging Data Fusion via Latent Space Learning for Alzheimer's Disease Diagnosis.Predictive Intelligence in Medicine. PRIME (Workshop) · 2018Article
- Conversion and time-to-conversion predictions of mild cognitive impairment using low-rank affinity pursuit denoising and matrix completion.Medical image analysis · 2018Article
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Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
In this paper, we aim to maximally utilize multimodality neuroimaging and genetic data to predict Alzheimer's disease (AD) and its prodromal status, i.e., a multi-status dementia diagnosis problem. Multimodality neuroimaging data such as MRI and PET provide valuable insights to abnormalities, and genetic data such as Single Nucleotide Polymorphism (SNP) provide information about a patient's AD risk factors. When used in conjunction, AD diagnosis may be improved. However, these data are heterogeneous (e.g., having different data distributions), and have different number of samples (e.g., PET data is having far less number of samples than the numbers of MRI or SNPs). Thus, learning an effective model using these data is challenging. To this end, we present a novel
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.