RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification

• URL: http://arxiv.org/abs/2309.04760v1
• Date: Sat, 9 Sep 2023 11:14:04 GMT
• Title: RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification
• Authors: Yizhe Zhang, Shuo Wang, Yejia Zhang, Danny Z. Chen
• Abstract summary: Conformal prediction (CP) generates a set of predictions for a given test sample. The size of the set indicates how certain the predictions are. We propose a new method called Reliable-Region-Based Conformal Prediction (RR-CP)
• Score: 24.52922162675259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e.g., 99.5\% of the time). CP provides comprehensive predictions on possible labels of a given test sample, and the size of the set indicates how certain the predictions are (e.g., a set larger than one is `uncertain'). Such distinct properties of CP enable effective collaborations between human experts and medical AI models, allowing efficient intervention and quality check in clinical decision-making. In this paper, we propose a new method called Reliable-Region-Based Conformal Prediction (RR-CP), which aims to impose a stronger statistical guarantee so that the user-specified error rate (e.g., 0.5\%) can be achieved in the test time, and under this constraint, the size of the prediction set is optimized (to be small). We consider a small prediction set size an important measure only when the user-specified error rate is achieved. Experiments on five public datasets show that our RR-CP performs well: with a reasonably small-sized prediction set, it achieves the user-specified error rate (e.g., 0.5\%) significantly more frequently than exiting CP methods.

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