How I failed machine learning in medical imaging -- shortcomings and recommendations

Gaël Varoquaux, Veronika Cheplygina

Research output: Working paperResearch


Medical imaging is an important research field with many opportunities for improving patients' health. However, there are a number of challenges that are slowing down the progress of the field as a whole, such optimizing for publication. In this paper we reviewed several problems related to choosing datasets, methods, evaluation metrics, and publication strategies. With a review of literature and our own analysis, we show that at every step, potential biases can creep in. On a positive note, we also see that initiatives to counteract these problems are already being started. Finally we provide a broad range of recommendations on how to further these address problems in the future. For reproducibility, data and code for our analyses are available on \url{}
Original languageEnglish
Publication statusPublished - 18 Mar 2021


  • eess.IV
  • cs.CV
  • cs.LG
  • stat.ML


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