ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2024
SOM2LM: Self-Organized Multi-Modal Longitudinal Maps.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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
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.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A survey of deep learning techniques in detecting neurological disorders using MRI.Biomedical engineering online · 2026Pooled it
- Multimodal Self-Supervised Learning for Early Alzheimer's: Cross-Modal MRI-PET, Longitudinal Signals, and Site Invariance.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Neuroimage modalities acquired by longitudinal studies often provide complementary information regarding disease progression. For example, amyloid PET visualizes the build-up of amyloid plaques that appear in earlier stages of Alzheimer's disease (AD), while structural MRIs depict brain atrophy appearing in the later stages of the disease. To accurately model multi-modal longitudinal data, we propose an interpretable self-supervised model called Self-Organized Multi-Modal Longitudinal Maps (SOM2LM). SOM2LM encodes each modality as a 2D self-organizing map (SOM) so that one dimension of each modality-specific SOMs corresponds to disease abnormality. The model also regularizes across modalities to depict their temporal order of capturing abnormality. When applied to longitudinal T1w MRIs and amyloid PET of the Alzheimer's Disease Neuroimaging Initiative (ADNI,
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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.