Upcoming events

Video analysis is a challenging task due to its high dimensionality and the complex, entangled spatio-temporal context. Applying this understanding to healthcare applications requires AI to align with humans’ expectations and values, i.e., making them responsible. In this seminar, I will introduce high-level geometric features, such as bounding boxes and human skeletal representations, for video analysis and explain how they reduce computational complexity, improve model generalizability, and facilitate the extraction of clinically relevant motion patterns. Furthermore, I will explore how incorporating geometric concepts enhances the interpretability, privacy, and fairness of deep learning models, fostering responsible AI. This is demonstrated through predicting Parkinson’s disease and cerebral palsy, as well as analyzing surgery videos.