Improving Peproducibility in Machine Learning Research

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Reproducibility and Good Practices in Machine Learning Research


Benjamin Haibe-Kains


While biostatistics and machine learning are essential to analyze biomedical data, researchers are facing multiple challenges around research reproducibility and transparency. It is essential for independent researchers to be able to scrutinize and reproduce the results of a study using its materials, and build upon them in future studies.

Computational reproducibility is achievable when the data can easily be shared and the required computational resources are relatively common. However, the complexity of current algorithms and their implementation, the need for specific computer hardware and the use of sensitive biomedical data represent major obstacles in healthy-related research. In this talk, Benjamin described the various aspects of a typical biomedical study that are necessary for reproducibility and the platforms that exist for sharing these materials with the scientific community.


  • Resource Sharing
  • Reproducibility