系统梳理医疗影像模型的偏差评估与数据漂移检测方法。
Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
- 分类梳理模型偏差与数据漂移的检测方法。
- 提出无真实标签时准确率估计的应对策略。
- 帮助临床部署实现长期稳定可靠预测。
机器学习（ML）模型在医疗影像分析中表现卓越，已达到专家水平。为提升其可信度、临床接受度与合规性，本文系统综述并分类了模型开发及生命周期内确保可靠性的方法。重点涵盖疾病分类模型中关于偏倚编码的内在机制评估与数据漂移检测方法。此外，针对显著漂移情况下的严重性评估，还介绍了在无真实标签可用时对分类器准确率的估计方法。这些工作有助于实践者实施保障模型可靠部署与持续性能一致的技术方案。
原文摘要 · Abstract (English)
Machine Learning (ML) models have gained popularity in medical imaging analysis given their expert level performance in many medical domains. To enhance the trustworthiness, acceptance, and regulatory compliance of medical imaging models and to facilitate their integration into clinical settings, we review and categorise methods for ensuring ML reliability, both during development and throughout the model's lifespan. Specifically, we provide an overview of methods assessing models' inner-workings regarding bias encoding and detection of data drift for disease classification models. Additionally, to evaluate the severity in case of a significant drift, we provide an overview of the methods developed for classifier accuracy estimation in case of no access to ground truth labels. This should enable practitioners to implement methods ensuring reliable ML deployment and consistent prediction performance over time.
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