{"slug": "contrastive-joint-embedding-prediction-for-representation-learning-in-structural", "title": "Contrastive Joint-Embedding Prediction for Representation Learning in Structural MRI", "summary": "Researchers introduced COJEPA, a self-supervised learning framework for volumetric brain MRI that combines joint-embedding predictive architecture with contrastive loss. Trained on 2,286 T1-weighted scans from two cohorts, COJEPA achieved top performance in twin retrieval, brain tumor segmentation, and age regression tasks, demonstrating effective representation learning without labeled data.", "body_md": "arXiv:2607.11962v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/contrastive-joint-embedding-prediction-for-representation-learning-in-structural", "canonical_source": "https://arxiv.org/abs/2607.11962", "published_at": "2026-07-15 04:00:00+00:00", "updated_at": "2026-07-15 04:00:53.995397+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision", "ai-research"], "entities": ["COJEPA", "HCP-YA", "AABC", "BraTS 2024", "OpenBHB", "I-JEPA"], "alternates": {"html": "https://wpnews.pro/news/contrastive-joint-embedding-prediction-for-representation-learning-in-structural", "markdown": "https://wpnews.pro/news/contrastive-joint-embedding-prediction-for-representation-learning-in-structural.md", "text": "https://wpnews.pro/news/contrastive-joint-embedding-prediction-for-representation-learning-in-structural.txt", "jsonld": "https://wpnews.pro/news/contrastive-joint-embedding-prediction-for-representation-learning-in-structural.jsonld"}}