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New pipeline audits identity duplication in large-scale neuroimaging data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed HAPPEN, a pipeline designed to audit identity duplication within large-scale neuroimaging repositories. This system uses SHA-256 fingerprinting to detect exact duplicates and a supervised contrastive retrieval method for identifying non-identical scans from the same individual. Deployed on a repository of over 95,000 scans, HAPPEN successfully identified numerous duplicate scan and subject groups, with a significant portion crossing dataset boundaries. The pipeline also demonstrated transferability to an independent institution without requiring model retraining. AI

IMPACT This research could improve the integrity of large-scale biomedical datasets, leading to more reliable AI-driven discoveries in neuroscience.

RANK_REASON The cluster describes a new research pipeline and its application in a scientific domain. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.CV →

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

New pipeline audits identity duplication in large-scale neuroimaging data

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The cluster describes a new research pipeline and its application in a scientific domain. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jiheng Li (HABS-HD), Michael E. Kim (HABS-HD), Trent M. Schwartz (HABS-HD), Yuhan Cui (HABS-HD), Gaurav Rudravaram (HABS-HD), Derek B. Archer (HABS-HD), Timothy J. Hohman (HABS-HD), Lori L. Beason-Held (HABS-HD), Victoria L. Morgan (HABS-HD), Dario J. En… ·

    Identity-Duplication Auditing in National-Scale Neuroimaging Repositories

    arXiv:2610.09614v1 Announce Type: new Abstract: National-scale magnetic resonance imaging (MRI) repositories increasingly integrate data from different studies and institutions. However, subject identifiers that are valid only within individual datasets are no longer guaranteed t…

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