Summary of A Conformalized Learning Of a Prediction Set with Applications to Medical Imaging Classification, by Roy Hirsch et al.
A conformalized learning of a prediction set with applications to medical imaging classification
by Roy Hirsch, Jacob Goldberger
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 algorithm modifies any medical imaging classifier to produce a prediction set with a user-specified probability, such as 90%, allowing for accurate and reliable predictions. By training a network to predict an instance-based version of the Conformal Prediction threshold, the algorithm ensures the required coverage while reducing the average size of the prediction set. Experimental results demonstrate the superiority of this approach over current methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve medical imaging classifiers by providing accurate and reliable predictions. The algorithm can modify any classifier to produce a prediction set with a specified probability, such as 90%. This means that doctors can be more confident in their diagnoses and make better treatment decisions. The algorithm uses machine learning to predict the Conformal Prediction threshold, which ensures the required coverage while reducing the size of the prediction set. |
Keywords
* Artificial intelligence * Machine learning * Probability
