Foundation models are rapidly being developed for computational pathology applications.
However, it remains an open question which factors are most important for downstream …

Abstract Self-Supervised Learning (SSL) is a valuable and robust training methodology for
contemporary Deep Neural Networks (DNNs), enabling unsupervised pretraining on a …

While image-text representation learning has become very popular in recent years, existing
models tend to lack spatial awareness and have limited direct applicability for dense …

Self-supervised learning (SSL) methods via joint embedding architectures have proven
remarkably effective at capturing semantically rich representations with strong clustering …

Coronary artery disease (CAD), one of the most common cause of mortality in the world.
Coronary artery calcium (CAC) scoring using computed tomography (CT) is key for risk …

Data augmentations play an important role in the recent success of Self-Supervised
Learning (SSL). While commonly viewed as encoding invariances into the learned …