Summary of Survrnc: Learning Ordered Representations For Survival Prediction Using Rank-n-contrast, by Numan Saeed et al.
SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast
by Numan Saeed, Muhammad Ridzuan, Fadillah Adamsyah Maani, Hussain Alasmawi, Karthik Nandakumar, Mohammad Yaqub
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This study proposes a novel method, Survival Rank-N Contrast (SurvRNC), to estimate cancer risk scores from medical images, electronic health records, and genomic data. The SurvRNC method introduces a loss function as a regularizer to learn ordinal representations based on survival times, handling censored data effectively. This approach can be incorporated into any survival model to improve performance. The proposed method was evaluated on the HECKTOR segmentation dataset and the outcome-prediction task dataset, achieving higher performance compared to state-of-the-art methods by 3.6% on the concordance index. The code is publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps predict cancer survival rates using medical images, health records, and genetic data. The new method learns how to rank data in order of importance based on when people survive or don’t survive. This makes it better at predicting outcomes. The researchers tested their method and showed it did a little better than other methods already out there. |
Keywords
* Artificial intelligence * Loss function
