Contrastive Joint-Embedding Prediction for Representation Learning in Structural MRI
Self-supervised learning is promising for medical imaging where labeled data is scarce. COJEPA combines joint-embedding predictive architecture and contrastive loss for volumetric brain MRI, trained on 2,286 T1-weighted scans. It achieves state-of-the-art results in twin retrieval, age regression, and tumor segmentation.
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[Submitted on 12 Jul 2026]
Title:Contrastive Joint-Embedding Prediction for Representation Learning in Structural MRI
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Abstract:Self-supervised learning offers a compelling approach for medical imaging, where labeled data are scarce and acquisition costs are high. We present COJEPA, a self-supervised framework for volumetric brain MRI that combines a joint-embedding predictive architecture (JEPA) with a contrastive loss (CO), targeting two complementary properties: local predictivity and global discriminability. The model is trained without labels on T1-weighted structural MRI from two cohorts (HCP-YA and AABC, $N{=}2286$, ages 22 to 90), extending I-JEPA to 3D with foreground-aware block masking, a hierarchical convolutional patch embedding, and world-space sinusoidal positional encodings. We evaluate all three objectives across zero-shot twin retrieval, brain tumor segmentation (BraTS 2024), and age regression (OpenBHB). COJEPA achieves the best monozygotic twin recall at rank@1 (0.84), the best finetuning age MAE (2.55 years on OpenBHB 3.0T), and matches CO on BraTS whole-tumor Dice, demonstrating that the combined objective yields representations that are simultaneously discriminative and locally structured.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.11962 [cs.CV]
(or arXiv:2607.11962v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.11962
arXiv-issued DOI via DataCite
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From: Fabian Mager [view email] [v1] Sun, 12 Jul 2026 10:48:13 UTC (3,954 KB)
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