End-to-end reproducible ai pipelines in radiology using the cloud

HIGHLIGHTS

• What: The authors show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. The authors demonstrate end-to-end reproducibility from retrieving cloudhosted data, through data pre-processing, deep learning inference, and postprocessing, to the analysis and reporting of the final results. The authors provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. The authors provide an overview of the re-implementation in Fig 1b.
• Who: Dennis Bontempi from the Data from Imaging Data Commons We retrieved two . . .
  

   

  

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