Exploring Cancer Progression: From Static Imaging Data to System Dynamics

Delve into the intricacies of cancer progression through a compelling 27-minute lecture by Heba Sailem from King's College London. Presented at the Fourth Symposium on Machine Learning and Dynamical Systems, this talk bridges the gap between static imaging data and dynamic system analysis in cancer research. Gain insights into cutting-edge methodologies that transform traditional static observations into dynamic models, potentially revolutionizing our understanding of cancer development and treatment strategies. Learn how machine learning techniques are being applied to extract temporal information from spatial data, offering a new perspective on tumor evolution and cellular interactions within the cancer microenvironment.