Explore the critical role of causality in medical imaging through this insightful lecture by Ben Glocker from Imperial College London. Delve into the challenges of data scarcity and data mismatch in medical image analysis, and discover how causal reasoning can provide new perspectives on these issues. Learn about the causal relationships between images, annotations, and data-collection processes, and their impact on predictive model performance and learning strategies. Examine surprising insights, such as the potential unsuitability of semi-supervision for image segmentation. Investigate real-world examples in skin lesion classification, brain tumor segmentation, and radiology reports. Gain knowledge on topics including semi-supervised learning, data augmentation, dataset shift, acquisition shift, domain adaptation, and domain generalization. Understand the importance of considering causal relationships in machine learning-based image analysis for improved success in clinical practice. Conclude with guidelines, regulations, and recommendations for implementing causal reasoning in medical imaging research and applications. Read more