Longitudinal self-supervised learning
Open accessAbstract
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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Of the three generative SSL studies, two reported comparisons with purely supervised models and showed relative improvements of 16.6% and 24.5% with self-supervised learning.
“Of the three studies, two reported comparisons with purely supervised models and showed relative improvements of 16.6%58 and 24.5%56 with self-supervised learning.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [58]
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Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equation
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Evaluating the Quality of Brain MRI Generators
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LSOR: Longitudinally-Consistent Self-Organized Representation Learning
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Latent Causal Modeling for 3D Brain MRI Counterfactuals
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LaTiM: Longitudinal Representation Learning in Continuous-Time Models to Predict Disease Progression
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- Authors
- Zhao, Q., Liu, Z., Adeli, E., & Pohl, K. M.
- Affiliations
- Stanford University (US)
- SRI International (US)
- Corresponding author
- Pohl, Kilian M.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 71
- Pages
- 102051
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC8184636
- OpenAlex
- W3144840690
- Cited by
- 71
- References
- 78
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 16 · 2023: 13 · 2022: 14 · 2021: 4
- FWCI
- 6.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NIAAA
- NIMH
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- longitudinal neuroimaging, self-supervised learning, brain age prediction, autoencoder, cosine loss, neurodegenerative diseases, neuropsychological disorders, representation learning, supervised classification
- MeSH terms
- Machine Learning, Supervised Machine Learning, Brain, Humans, Magnetic Resonance Imaging
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