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Deep learning in medical applications usually faces the problem of limited training data that does not represent most of the relevant distribution. This is due to the fact that, both, the acquisition process of the data is difficult and the labeling of medical data is costly. The former is caused by the complex and expensive nature of medical sensors and strict data privacy laws. The latter is due to the high wages of medical professionals.

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Abstract: TSynD Targeted Synthetic Data Generation for Enhanced Medical Image Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Joshua Niemeijer,
  • Jan Ehrhardt,
  • Hristina Uzunova,
  • Heinz Handels

摘要

Deep learning in medical applications usually faces the problem of limited training data that does not represent most of the relevant distribution. This is due to the fact that, both, the acquisition process of the data is difficult and the labeling of medical data is costly. The former is caused by the complex and expensive nature of medical sensors and strict data privacy laws. The latter is due to the high wages of medical professionals.