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Every day, thousands of oncology data points are collected that have the potential to unlock better cancer treatments for all. Machine learning models can power the discovery of relevant insights at a scale unachievable by humans, yet to achieve this they require access to large volumes of highly diverse datasets. However, with challenges relating to data locations, heterogeneity, data regulations and competition, sharing insights on healthcare data is difficult, and collaboration between healthcare stakeholders harder. Federated learning stands as a solution – as a viable means to empower cooperation and improve patient outcomes in oncology while keeping sensitive health data safe and secure. Read this white paper to learn more about the power of privacy-preserving machine learning techniques such as federated learning to unlock the full potential of healthcare data in oncology. |
