Longitudinal Self Supervised Learning

Longitudinal self-supervised learning (LSSL) leverages temporal patterns in sequential data, like medical images, to learn representations without explicit labels, aiming to improve diagnostic accuracy and disease progression modeling. Current research focuses on applying LSSL to various medical imaging tasks, employing architectures such as Siamese networks and neural ordinary differential equations to capture dynamic changes over time. This approach addresses the challenge of limited labeled data in medical imaging, offering a powerful tool for analyzing disease progression and potentially improving patient care through earlier and more precise diagnoses.