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Research questions transferability metrics in medical imaging

By PulseAugur Editorial · [1 sources] ·

A new research paper investigates the robustness of transferability estimation (TE) metrics, which aim to predict the best source model for transfer learning, particularly in medical imaging. The study highlights that small changes in the target dataset, such as sample size and random seeds, can significantly alter model rankings. Furthermore, the choice of evaluation metric used for reference rankings also impacts the assessment of TE metrics, leading to low agreement between TE metric rankings and actual performance. AI

IMPACT Highlights potential unreliability in model selection methods for medical AI, impacting deployment and research.

RANK_REASON The cluster contains a research paper published on arXiv discussing methodology in machine learning 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 →

Research questions transferability metrics in medical imaging

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The cluster contains a research paper published on arXiv discussing methodology in machine learning 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) · Niclas Cla{\ss}en, Th\'eo Sourget, Dovile Juodelyte, Rob van der Goot, Veronika Cheplygina ·

    Robustness of transferability estimation metrics for medical imaging

    arXiv:2608.09999v1 Announce Type: cross Abstract: In transfer learning, the choice of source model largely influences the performance on a target dataset. Still, selecting a fitting source remains a challenging task, especially in medical imaging where one has to decide between m…

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