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Foundation models retain significant patient info in brain MRI embeddings

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv investigates the privacy risks associated with using foundation models for brain MRI data. Researchers found that even when raw scans are replaced with learned embeddings, significant patient-specific information remains, enabling high rates of patient linkage and sensitive attribute inference. The findings indicate that relying solely on embeddings is insufficient for anonymizing medical imaging data. AI

IMPACT Highlights the need for enhanced privacy measures beyond embedding release for sensitive medical data.

RANK_REASON Research paper published on arXiv detailing privacy risks in foundation models for medical imaging. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Foundation models retain significant patient info in brain MRI embeddings

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Research paper published on arXiv detailing privacy risks in foundation models for medical imaging. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Imen Bajar, H\'eber Hwang Arcolezi, Antoine Boutet, Carole Frindel, Mohamed Maouche ·

    Quantifying privacy risks in foundation model representations of brain MRI

    arXiv:2610.20494v1 Announce Type: new Abstract: Pretrained representations are increasingly used to share and reuse information from medical images without exposing the original scans, but it is unclear how much patient-specific information these representations retain. We invest…

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