Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification
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.