Trustworthy Machine Learning in Medical Imaging

Intelligent medical systems capable of capturing and interpreting sensor data and providing context-aware assistance promise to revolutionize interventional healthcare. However, flaws in common practice as well as a lack of standardization in the field of medical image analysis substantially impede successful adoption of modern ML research into clinical use. Drawing from research within my own group as well as large international expert consortia, I will discuss pervasive shortcomings in current medical imaging procedures -- specifically focusing on the three core aspects of image acquisition, image analysis, and algorithm validation – as well as present possible solutions. My talk will showcase the importance of systematically professionalizing every aspect of the medical imaging pipeline to the end of readying intelligent imaging systems for clinical use.