医学影像分类中，置信区间预测在分布偏移下不可靠，需警惕误用风险。
Pitfalls of Conformal Predictions for Medical Image Classification
- 通过皮肤和病理图像案例揭示分布偏移下置信预测失效
- 小类别数量场景下实际应用价值有限，无法提升准确率
- 不适用于特定类别或患者特征子集，易产生误导
可靠的不确定性估计是医学分类任务中的主要挑战之一。尽管已有多种方法提出，近年来基于统计的置信区间预测因其可证明的校准保证而受到广泛关注。然而，在医疗等安全关键领域应用时，该方法存在陷阱、局限性及隐含假设，使用者需保持警惕。我们通过皮肤科与组织病理学的实例表明，当输入或标签变量发生分布偏移时，置信区间预测不可靠。此外，不应使用置信区间预测来筛选样本以提高准确率，也不适用于数据子集（如特定类别或患者属性）。尤其在类别数量较少的医学图像分类任务中，其实际应用价值极为有限。
原文摘要 · Abstract (English)
Reliable uncertainty estimation is one of the major challenges for medical classification tasks. While many approaches have been proposed, recently the statistical framework of conformal predictions has gained a lot of attention, due to its ability to provide provable calibration guarantees. Nonetheless, the application of conformal predictions in safety-critical areas such as medicine comes with pitfalls, limitations and assumptions that practitioners need to be aware of. We demonstrate through examples from dermatology and histopathology that conformal predictions are unreliable under distributional shifts in input and label variables. Additionally, conformal predictions should not be used for selecting predictions to improve accuracy and are not reliable for subsets of the data, such as individual classes or patient attributes. Moreover, in classification settings with a small number of classes, which are common in medical image classification tasks, conformal predictions have limited practical value.
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