# Longitudinal Self-Supervised Learning

arXiv: https://arxiv.org/abs/2006.06930 · HTML: https://mlsanity.com/paper/2006.06930/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 30 of 30)

- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model](https://mlsanity.com/paper/2212.08034/) (2022)
- [Self-Supervised Longitudinal Neighbourhood Embedding](https://mlsanity.com/paper/2103.03840/) (2021)
- [Learning to Compare Longitudinal Images](https://mlsanity.com/paper/2304.02531/) (2023)
- [Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIs](https://mlsanity.com/paper/2310.04630/) (2023)
- [Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare](https://mlsanity.com/paper/2203.11132/) (2022)
- [Towards Generalisable Foundation Models for Brain MRI](https://mlsanity.com/paper/2510.23415/) (2025)
- [Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation](https://mlsanity.com/paper/2512.09185/) (2025)
- [Latent Causal Modeling for 3D Brain MRI Counterfactuals](https://mlsanity.com/paper/2409.05585/) (2024)
- [Longitudinal Correlation Analysis for Decoding Multi-Modal Brain Development](https://mlsanity.com/paper/2107.04724/) (2021)
- [Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equation](https://mlsanity.com/paper/2310.10431/) (2023)
- [Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus Images](https://mlsanity.com/paper/2304.11168/) (2023)
- [LSOR: Longitudinally-Consistent Self-Organized Representation Learning](https://mlsanity.com/paper/2310.00213/) (2023)
- [Evaluation of 3D Counterfactual Brain MRI Generation](https://mlsanity.com/paper/2508.02880/) (2025)
- [Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs](https://mlsanity.com/paper/2604.22618/) (2026)
- [Longitudinal Self-Supervision for COVID-19 Pathology Quantification](https://mlsanity.com/paper/2203.10804/) (2022)
- [CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis](https://mlsanity.com/paper/2207.02091/) (2022)
- [XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning](https://mlsanity.com/paper/2207.13223/) (2022)
- [Detection of diabetic retinopathy using longitudinal self-supervised learning](https://mlsanity.com/paper/2209.00915/) (2022)
- [Learning Temporally Equivariance for Degenerative Disease Progression in OCT by Predicting Future Representations](https://mlsanity.com/paper/2405.09404/) (2024)
- [Beyond Random Partitioning: Unsupervised Spatio-Temporal Stratification for Cohort Balancing in Longitudinal Medical Imaging](https://mlsanity.com/paper/2608.00073/) (2026)
- [Parcel2Progression: An Anatomy-aware Longitudinal Framework for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2608.08753/) (2026)
- [Variational Inference for Longitudinal Data Using Normalizing Flows](https://mlsanity.com/paper/2303.14220/) (2023)
- [Shape Modeling of Longitudinal Medical Images: From Diffeomorphic Metric Mapping to Deep Learning](https://mlsanity.com/paper/2503.21489/) (2025)
- [The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound](https://mlsanity.com/paper/2504.07904/) (2025)
- [Self-Supervised Cross-Encoder for Neurodegenerative Disease Diagnosis](https://mlsanity.com/paper/2509.07623/) (2025)
- [Is Complete Labeling Necessary? Understanding Active Learning in Longitudinal Medical Imaging](https://mlsanity.com/paper/2511.18007/) (2025)
- [A Cautionary Tale of Self-Supervised Learning for Imaging Biomarkers: Alzheimer's Disease Case Study](https://mlsanity.com/paper/2601.16467/) (2026)
- [BrainNorm: A Foundation Model that knows Normal via Semantic Atlas Pretraining](https://mlsanity.com/paper/2608.17521/) (2026)
- [Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum](https://mlsanity.com/paper/2608.19436/) (2026)

## Abstract (arXiv)

Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires a 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.