Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.05037 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914906827128832 |
|---|---|
| author | Hirsch, Roy Goldberger, Jacob |
| author_facet | Hirsch, Roy Goldberger, Jacob |
| contents | Medical imaging classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, which prevents their deployment in medical clinics. We present an algorithm that can modify any classifier to produce a prediction set containing the true label with a user-specified probability, such as 90%. We train a network to predict an instance-based version of the Conformal Prediction threshold. The threshold is then conformalized to ensure the required coverage. We applied the proposed algorithm to several standard medical imaging classification datasets. The experimental results demonstrate that our method outperforms current approaches in terms of smaller average size of the prediction set while maintaining the desired coverage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05037 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A conformalized learning of a prediction set with applications to medical imaging classification Hirsch, Roy Goldberger, Jacob Machine Learning Medical imaging classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, which prevents their deployment in medical clinics. We present an algorithm that can modify any classifier to produce a prediction set containing the true label with a user-specified probability, such as 90%. We train a network to predict an instance-based version of the Conformal Prediction threshold. The threshold is then conformalized to ensure the required coverage. We applied the proposed algorithm to several standard medical imaging classification datasets. The experimental results demonstrate that our method outperforms current approaches in terms of smaller average size of the prediction set while maintaining the desired coverage. |
| title | A conformalized learning of a prediction set with applications to medical imaging classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.05037 |