提出概率鲁棒性评估框架，更真实地衡量医疗图像模型的可信度。
Probabilistic Robustness in Medical Image Classification
- 构建自然退化场景，模拟真实医疗影像中的干扰
- 在MedMNIST v2上系统评估主流模型性能下降程度
- 为临床部署提供可量化的模型可信度分析方法
深度学习在医疗图像分类中表现优异，但在安全关键的临床环境中，其可靠部署仍面临挑战，因扰动下的预测错误可能造成严重后果。现有研究多从最坏情况出发关注对抗鲁棒性，但此类设定在真实医疗应用中代表性不足。本文提出概率鲁棒性（PR）作为更贴近实际的模型可信度度量方式。为此，我们为医疗图像分类构建了一套自然退化设置，并在MedMNIST v2数据集上系统评估了常用深度学习模型的表现。研究提供了基于统计学的模型可信度评估视角，有助于推动深度学习模型在医疗影像领域的更可信应用。
原文摘要 · Abstract (English)
Deep learning (DL) has shown strong performance in medical image classification, but its trustworthy deployment remains challenging in safety-critical clinical settings, where prediction errors under perturbations may lead to severe consequences. Existing studies mainly focus on adversarial robustness (AR) from a worst-case perspective; however, such settings may be less representative of real medical applications. In this work, we investigate probabilistic robustness (PR) as a more practical measure of model trustworthiness. To this end, we construct a set of natural corruption settings for medical image classification and systematically evaluate commonly used DL models on MedMNIST v2 dataset. Our study provides a statistically grounded perspective on assessing the trustworthiness of DL models, thereby supporting their more trustworthy deployment in medical imaging applications.
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