Explore a cutting-edge approach to improving machine learning model generalization in medical imaging through a 52-minute conference talk by Rikiya Yamashita from Stanford University. Delve into STRAP (Style TRansfer Augmentation for histoPathology), a novel data augmentation technique that uses non-medical artistic paintings to create domain-agnostic visual representations in computational pathology. Learn how this method enhances model robustness to domain shifts and achieves state-of-the-art performance in pathology classification tasks. Gain insights into the challenges of applying machine learning to medical imaging and discover potential solutions for improving clinical applicability. Understand the speaker's unique perspective as a radiologist turned applied research scientist and how this dual expertise contributes to bridging the gap between machine learning and clinical medicine.