Medical imaging plays a vital role in diagnosing and treating various health conditions, but it also raises significant privacy concerns as sensitive personal information can be contained within these images. Differential privacy, a privacy-preserving artificial intelligence technique, offers a solution to these challenges and enable the secure analysis of medical images while protecting patient privacy.

In this talk, we will focus on the potential of differential privacy in medical imaging. We will explore its various applications, including disease detection, diagnosis, and treatment planning, and discuss its ethical implications. We will also examine the technical aspects of differential privacy, including its implementation in machine learning algorithms, such as deep learning, and its limitations and challenges.

Furthermore, we will highlight some of our ongoing research and development efforts in this area, including recent advancements in differentially private deep learning for medical imaging. We will discuss the trade-offs between privacy and utility in these applications and provide insights on how to achieve a balance between the two.

Attendees will gain a deeper understanding of the potential and challenges of differential privacy in medical imaging and its implications for healthcare.