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

• URL: http://arxiv.org/abs/2407.06852v1
• Date: Tue, 9 Jul 2024 13:41:32 GMT
• Title: TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression Analysis
• Authors: Jacob Thrasher, Alina Devkota, Ahmed Tafti, Binod Bhattarai, Prashnna Gyawali,
• Abstract summary: Alzheimer's Dementia (AD) represents one of the most pressing challenges in the field of neurodegenerative disorders. Recent advancements in deep learning and various representation learning strategies, including self-supervised learning (SSL), have shown significant promise in enhancing medical image analysis. 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.
• Score: 6.6584447062231895
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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