  Alzheimer's Dementia (AD) represents one of the most pressing challenges in
the field of neurodegenerative disorders, with its progression analysis being
crucial for understanding disease dynamics and developing targeted
interventions. Recent advancements in deep learning and various representation
learning strategies, including self-supervised learning (SSL), have shown
significant promise in enhancing medical image analysis, providing innovative
ways to extract meaningful patterns from complex data. Notably, the computer
vision literature has demonstrated that incorporating supervisory signals into
SSL can further augment model performance by guiding the learning process with
additional relevant information. However, the application of such supervisory
signals in the context of disease progression analysis remains largely
unexplored. This gap is particularly pronounced given the inherent challenges
of incorporating both event and time-to-event information into the learning
paradigm. Addressing this, we propose a novel framework, Time and Even-aware
SSL (TE-SSL), which integrates time-to-event and event data as supervisory
signals to refine the learning process. Our comparative analysis with existing
SSL-based methods in the downstream task of survival analysis shows superior
performance across standard metrics.
