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<p>Magnetic Resonance Imaging is a critical imaging modality in clinical diagnosis and research, yet its complexity and heterogeneity hinder scalable, generalizable machine learning. Although foundation models have revolutionized language and vision tasks, their application to MRI remains constrained by data scarcity and narrow anatomical focus. We present Decipher-MR, a 3D MRI-specific vision-language foundation model trained on 200,000 MRI series from over 22,000 studies spanning diverse anatomical regions, sequences, and pathologies. Decipher-MR integrates self-supervised vision learning with report-guided text supervision to build robust representations for broad applications. To enable efficient use, Decipher-MR supports a modular design that enables tuning of lightweight, task-specific decoders attached to a frozen pretrained encoder. Following this setting, we evaluate Decipher-MR across disease classification, demographic prediction, anatomical localization, and cross-modal retrieval, demonstrating consistent improvements over existing foundation models and task-specific approaches. These results support Decipher-MR as a promising and reusable foundation for MRI-based AI, within the scope of the tasks and datasets evaluated.</p>

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Decipher-MR: a vision-language foundation model for 3D MRI representations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月4日

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  • Zhijian Yang,
  • Noel DSouza,
  • Istvan Megyeri,
  • Xiaojian Xu,
  • Amin Honarmandi Shandiz,
  • Farzin Haddadpour,
  • Krisztian Koos,
  • Laszlo Rusko,
  • Emanuele Valeriano,
  • Bharadwaj Swaminathan,
  • Lei Wu,
  • Parminder Bhatia,
  • Taha Kass-Hout,
  • Erhan Bas

摘要

Magnetic Resonance Imaging is a critical imaging modality in clinical diagnosis and research, yet its complexity and heterogeneity hinder scalable, generalizable machine learning. Although foundation models have revolutionized language and vision tasks, their application to MRI remains constrained by data scarcity and narrow anatomical focus. We present Decipher-MR, a 3D MRI-specific vision-language foundation model trained on 200,000 MRI series from over 22,000 studies spanning diverse anatomical regions, sequences, and pathologies. Decipher-MR integrates self-supervised vision learning with report-guided text supervision to build robust representations for broad applications. To enable efficient use, Decipher-MR supports a modular design that enables tuning of lightweight, task-specific decoders attached to a frozen pretrained encoder. Following this setting, we evaluate Decipher-MR across disease classification, demographic prediction, anatomical localization, and cross-modal retrieval, demonstrating consistent improvements over existing foundation models and task-specific approaches. These results support Decipher-MR as a promising and reusable foundation for MRI-based AI, within the scope of the tasks and datasets evaluated.