Study reveals ML researchers' intuitive transfer learning choices in medical imaging

A new study published on arXiv explores the intuitive decision-making processes of machine learning researchers when selecting source datasets for transfer learning in medical image classification. The research, conducted via a survey, reveals that practitioners' choices are influenced by task dependency, community norms, dataset characteristics, and perceived similarity, rather than solely by systematic principles. Notably, the study found a disconnect between similarity ratings and expected performance, and a general lack of consideration for ethical and fairness implications in dataset selection. AI

IMPACT Highlights a gap in systematic source dataset selection for medical imaging transfer learning, suggesting a need for better tools and frameworks to improve generalizability and patient outcomes.

RANK_REASON This is a research paper published on arXiv detailing a study on machine learning practitioners' methods. [lever_c_demoted from research: ic=1 ai=1.0]

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• Amelia Jiménez-Sánchez
• arXiv

• paper
• other

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