Confronting the Reproducibility Crisis in Machine Learning: Navigating Best Practices and Avoiding Pitfalls

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.

Objectives

• 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.

Additional Resources

• Blind Spots: Insights From The June 2024 Webinar On Confronting The Reproducibility Crisis In Machine Learning

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).