ArticleIEEE journal of biomedical and health informatics2018
Multi-Hypergraph Learning for Incomplete Multimodality Data.
Article in IEEE journal of biomedical and health informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- Improving Late-Life Depression Analysis with Collaborative Domain Adaptation: Learning from Heterogeneous Structural MRI.Reconstruction and imaging motion estimation, and graphs in biomedical image analysis : first international workshop, RIME 2025 and 7th international workshop, GRAIL 2025, Daejeon, South Korea, September 27, 2025 : proceedings. RIME (Wo... · 2026Article
- GAMMA-PD: Graph-based Analysis of Multi-Modal Motor Impairment Assessments in Parkinson's Disease.Graphs in biomedical image analysis : 6th International Workshop, GRAIL 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings. GRAIL (Workshop) (6th : 2024 : Marrakesh, Morocco) · 2025Article
- Article
- Correlation Guided Graph Learning to Estimate Functional Connectivity Patterns From fMRI Data.IEEE transactions on bio-medical engineering · 2021Article
- Bidirectional Mapping of Brain MRI and PET With 3D Reversible GAN for the Diagnosis of Alzheimer's Disease.Frontiers in neuroscience · 2021Article
- Deep learning methods and applications in neuroimaging.Journal of neuroscience methods · 2020Article
- Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data.IEEE transactions on medical imaging · 2020Article
- Synthesizing Missing PET from MRI with Cycle-consistent Generative Adversarial Networks for Alzheimer's Disease Diagnosis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Multi-modality data convey complementary information that can be used to improve the accuracy of prediction models in disease diagnosis. However, effectively integrating multi-modality data remains a challenging problem, especially when the data are incomplete. For instance, more than half of the subjects in the Alzheimer's disease neuroimaging initiative (ADNI) database have no fluorodeoxyglucose positron emission tomography and cerebrospinal fluid data. Currently, there are two commonly used strategies to handle the problem of incomplete data: 1) discard samples having missing features; and 2) impute those missing values via specific techniques. In the first case, a significant amount of useful information is lost and, in the second case, additional noise and artifacts might be introduced into the data. Also, previous studies generally focus on the pairwise relationships among subjects, without considering their underlying complex (e.g., high-order) relationships. To address these issues, in this paper, we propose a multi-hypergraph learning method for dealing with incomplete multimodality data. Specifically, we first construct multiple hypergraphs to represent the high-order relationships among subjects by dividing them into several groups according to the availability of their data modalities. A hypergraph regularized transductive learning method is then applied to these groups for automatic diagnosis of brain diseases. Extensive evaluation of the proposed method using all subjects in the baseline ADNI database indicates that our method achieves promising results in AD/MCI classification, compared with the state-of-the-art methods.
Indexed as
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.