Summary of A Unified Model For Longitudinal Multi-modal Multi-view Prediction with Missingness, by Boqi Chen et al.
A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness
by Boqi Chen, Junier Oliva, Marc Niethammer
First submitted to arxiv on: 18 Mar 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 A novel approach is presented for analyzing longitudinal medical records, which often consist of multiple modalities such as images, text, and tabular information. The proposed unified model tackles the challenges of missing data by allowing input from any number of timepoints and leveraging all available data, regardless of its availability. The method is evaluated on a knee osteoarthritis dataset, demonstrating its effectiveness compared to specific models using the same modality and view combinations during training and evaluation. The study highlights the benefits of extended temporal data and provides insights into the importance of each modality/view for different tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to analyze medical records that include images, text, and other types of information. These records are often used to understand how diseases progress over time. The challenge is that some patients may not have all of their data available at the same time. To solve this problem, the team created a single model that can use any amount of data from any point in time. They tested their approach on a dataset related to knee osteoarthritis and found it was more accurate than other methods. This new approach has the potential to help doctors better understand and treat diseases. |
