Join us as we explore the core challenges and solutions in enhancing the reproducibility and generalizability of machine learning projects. This webinar aims to equip researchers, data scientists, and practitioners with effective strategies to facilitate data accessibility, prevent data leakage, and standardize data preprocessing. Discover the significance of choosing appropriate algorithms, promoting code sharing, and managing dependencies to ensure reliable and reproducible machine learning outcomes. The challenge of reproducibility and generalizability is particularly critical, as these factors directly impact the clinical adoption of machine learning tools, influencing how these technologies can be trusted and scaled in healthcare settings. Whether you are developing machine learning models or managing complex datasets, this webinar will provide you with the insights needed to improve your practices and adhere to high standards in developing generalizable machine learning approaches.


  • Equip participants with strategies to facilitate data accessibility, prevent data leakage, and standardize data preprocessing to enhance the reproducibility of machine learning projects.
  • Provide insights into selecting appropriate algorithms, promoting code sharing, and managing dependencies effectively to achieve reliable machine learning outcomes.
  • Advocate for the adoption of comprehensive and transparent research documentation standards to improve the clarity and reproducibility of machine learning research.
  • Demonstrate how to utilize advanced tools and frameworks and adhere to community standards to significantly improve the reproducibility of machine learning systems.

CE Credit

SIIM has approved this activity for 1.0 hours of SIIM IIP Credits towards certification and re-certification by the American Board of Imaging Informatics (ABII).

To receive credit, registrants must view the entire webinar and then complete the post-webinar survey. Webinar credits will only be awarded one time per webinar view, regardless of if the learner watches the content live or on-demand.

Note: In order to receive credits for any events/learning you attend you must select your eligible credit types found in the CE & Certification section of your MySIIM Account profile.

To access the webinar, once you are registered navigate to My Learning in your My SIIM Account profile.    

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Jun 5, 2024

Credit Amount


Credit Type


Non-Member Cost


Member Cost


Delivery Type

  • Virtual-Live

Audience Type

  • Clinician
  • Researcher/Scientist

Farhad Maleki, PhD

Assistant Professor

University of Calgary

Zahra Azizi, MD, MS

Clinical Scientist

Stanford University


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