Summary of Multi-modal Data Binding For Survival Analysis Modeling with Incomplete Data and Annotations, by Linhao Qu et al.
Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations
by Linhao Qu, Dan Huang, Shaoting Zhang, Xiaosong Wang
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Survival analysis plays a crucial role in cancer treatment research, requiring accurate patient survival rate predictions. Recent advancements in data collection have enabled integrating information from various sources to enhance predictive power. However, real-world scenarios often involve incomplete data, particularly censored survival labels, which can introduce bias and limit model efficacy. To address this gap, we propose a novel framework that simultaneously handles incomplete data across modalities and censored survival labels. Our approach employs advanced foundation models to encode individual modalities and align them into a universal representation space for seamless fusion. By generating pseudo labels and incorporating uncertainty, we significantly improve predictive accuracy. The proposed method demonstrates outstanding prediction accuracy in two survival analysis tasks on both employed datasets. This innovative approach overcomes limitations associated with disparate modalities and improves the feasibility of comprehensive survival analysis using multiple large foundation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cancer treatment relies on accurate predictions about patient survival rates. To improve these predictions, scientists have been collecting data from different sources. However, this data can be incomplete or missing, which makes it harder to make accurate predictions. Researchers have developed a new way to handle incomplete data and improve predictive accuracy. They use advanced computer models to combine information from multiple sources into one universal representation space. By generating fake labels and accounting for uncertainty, they achieve better prediction results. This approach helps overcome limitations associated with using different data sources and improves the feasibility of comprehensive survival analysis. |
