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GenFAR framework learns generalized brain representations from 49,246 MRIs

By PulseAugur Editorial · [2 sources] ·

Researchers have developed GenFAR, a novel deep learning framework designed to create generalized, clinically informed feature representations from brain MRIs. This modular architecture was trained on a large dataset of 49,246 individuals across 11 cohorts, utilizing 17 diverse tasks to capture rich brain representations. The framework employs a sequential learning approach, identifying an optimal sequence of six tasks and a 'Donor Score' metric to quantify task contributions, ultimately enhancing the sample efficiency and accuracy of downstream deep learning models. AI

IMPACT Enhances sample efficiency and accuracy for downstream deep learning tasks in neuroimaging.

RANK_REASON The cluster describes a research paper detailing a new deep learning framework for neuroimaging analysis.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

GenFAR framework learns generalized brain representations from 49,246 MRIs

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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    GenFAR: A generalized representation of brain structure, derived from 49,246 multi-cohort MRIs via deep learning

    Deep learning models for neuroimaging have largely been developed for individual tasks, limiting knowledge transfer across applications. Here we introduce GenFAR, a modular deep learning framework that learns general, clinically informed features from brain MRIs. We trained this …

  2. arXiv cs.CV TIER_1 English(EN) · Vishnu M. Bashyam, Guray Erus, Junhao Wen, Pratik Chaudhari, Randa Melhem, Sindhuja Govindarajan Tirumalai, Gareth Harman, Yong Fan, Colin L. Masters, Paul Maruff, Sterling C. Johnson, Jurgen Fripp, Duygu Tosun, John C. Morris, Daniel S. Marcus, Pamela L… ·

    GenFAR: A generalized representation of brain structure, derived from 49,246 multi-cohort MRIs via deep learning

    arXiv:2608.12185v1 Announce Type: new Abstract: Deep learning models for neuroimaging have largely been developed for individual tasks, limiting knowledge transfer across applications. Here we introduce GenFAR, a modular deep learning framework that learns general, clinically inf…

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