Summary of Te-ssl: Time and Event-aware Self Supervised Learning For Alzheimer’s Disease Progression Analysis, by Jacob Thrasher et al.
TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer’s Disease Progression Analysis
by Jacob Thrasher, Alina Devkota, Ahmed Tafti, Binod Bhattarai, Prashnna Gyawali
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper aims to develop a novel framework for analyzing Alzheimer’s Dementia (AD) progression using deep learning and self-supervised learning (SSL). The authors leverage recent advancements in computer vision, incorporating supervisory signals into SSL to enhance model performance. A novel approach called Time and Event-aware SSL (TE-SSL) is proposed, which integrates time-to-event and event data as supervisory signals to refine the learning process. The paper presents a comparative analysis with existing SSL-based methods for survival analysis, demonstrating superior performance across standard metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are working on a new way to analyze Alzheimer’s disease. They’re using computer algorithms to study brain scans and other medical images. Their approach is called Time and Event-aware Self-Supervised Learning (TE-SSL). It helps the algorithm learn from data by giving it clues about what’s important. This can help the algorithm make better predictions about when Alzheimer’s will progress. The new method performs well compared to existing approaches. |
Keywords
* Artificial intelligence * Deep learning * Self supervised
