TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression Analysis

TL;DR

TE-SSL introduces a novel self-supervised learning framework that incorporates time-to-event and event data to improve Alzheimer's disease progression analysis, demonstrating superior survival prediction performance.

Contribution

The paper presents TE-SSL, a new self-supervised learning method that effectively integrates temporal and event information for disease progression modeling.

Findings

TE-SSL outperforms existing SSL methods in survival analysis tasks.

Incorporating event and time data enhances model accuracy.

TE-SSL achieves superior predictive performance on Alzheimer's progression data.

Abstract

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.