ArticleMedical image analysis2021
Longitudinal self-supervised learning.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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
29 citing papers in PubMed.
- Forecasting Alzheimer's disease progression via identity-preserved denoising diffusion generative adversarial network.NPJ digital medicine · 2026Article
- Towards generalisable foundation models for brain MRI.Npj imaging · 2026Article
- A three-dimensional multi-modal foundation model for optical coherence tomography.Nature biomedical engineering · 2026Article
- Mild Cognitive Impairment Detection System Based on Unstructured Spontaneous Speech: Longitudinal Dual-Modal Framework.JMIR medical informatics · 2026Article
- Evaluation of 3D Counterfactual Brain MRI Generation.Deep generative models : 5th MICCAI workshop, DGM4MICCAI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings. DGM4MICCAI (Workshop) (5th : 2025 : Taejon-si, Korea) · 2026Article
- Latent Causal Modeling for 3D Brain MRI Counterfactuals.Deep generative models : 5th MICCAI workshop, DGM4MICCAI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings. DGM4MICCAI (Workshop) (5th : 2025 : Taejon-si, Korea) · 2026Article
- Surface-based Multi-Axis Longitudinal Disentanglement Using Contrastive Learning for Alzheimer's Disease.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Multimodal contrastive learning on rs-fMRI to quantify whole-brain network recovery after hypothalamic hamartoma surgery.Biomedical engineering online · 2025Article
- Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics.JMIR AI · 2025Article
- The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound.Bioengineering (Basel, Switzerland) · 2025Article
- Enhancing 3D dopamine transporter imaging as a biomarker for Parkinson's disease via self-supervised learning with diffusion models.Cell reports. Medicine · 2025Article
- Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Generalizable self-supervised learning for brain CTA in acute stroke.Computers in biology and medicine · 2025Article
- Contrastive self-supervised learning for neurodegenerative disorder classification.Frontiers in neuroinformatics · 2025Article
- Neurocognitive Latent Space Regularization for Multi-Label Diagnosis from MRI.Predictive Intelligence in Medicine. PRIME (Workshop) · 2025Article
- Shape modeling of longitudinal medical images: from diffeomorphic metric mapping to deep learning.Frontiers in artificial intelligence · 2025Review
- Metadata-conditioned generative models to synthesize anatomically-plausible 3D brain MRIs.Medical image analysis · 2024Article
- SOM2LM: Self-Organized Multi-Modal Longitudinal Maps.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Evaluating the Quality of Brain MRI Generators.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound.BMC medical imaging · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires large number of ground-truth labels to be informative. As ground-truth labels are often missing or expensive to obtain in neuroscience, we avoid them in our analysis by combing factor disentanglement with self-supervised learning to identify changes and consistencies across the multiple MRIs acquired of each individual over time. Specifically, we propose a new definition of disentanglement by formulating a multivariate mapping between factors (e.g., brain age) associated with an MRI and a latent image representation. Then, factors that evolve across acquisitions of longitudinal sequences are disentangled from that mapping by self-supervised learning in such a way that changes in a single factor induce change along one direction in the representation space. We implement this model, named Longitudinal Self-Supervised Learning (LSSL), via a standard autoencoding structure with a cosine loss to disentangle brain age from the image representation. We apply LSSL to two longitudinal neuroimaging studies to highlight its strength in extracting the brain-age information from MRI and revealing informative characteristics associated with neurodegenerative and neuropsychological disorders. Moreover, the representations learned by LSSL facilitate supervised classification by recording faster convergence and higher (or similar) prediction accuracy compared to several other representation learning techniques.
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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.