Summary of Deep Learning with Noisy Labels in Medical Prediction Problems: a Scoping Review, by Yishu Wei et al.
Deep learning with noisy labels in medical prediction problems: a scoping review
by Yishu Wei, Yu Deng, Cong Sun, Mingquan Lin, Hongmei Jiang, Yifan Peng
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper conducts a comprehensive scoping review of label noise management in deep learning-based medical prediction problems. The objective is to identify the current state-of-the-art methods for detecting, handling, and evaluating label noise in medical datasets. The review includes research on label uncertainty and its impact on deep learning models used in medical diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how doctors’ notes can be noisy or incorrect when training AI machines that predict medical conditions. It’s like trying to make a good recipe with bad ingredients! To fix this, researchers need to know what methods are working best to deal with this noise. This paper reviews all the research on making sure AI models don’t get fooled by bad labels. |
Keywords
* Artificial intelligence * Deep learning
