用贝叶斯方法减少医疗图像分类中的人工标注成本。
Bayesian Inference for Correlated Human Experts and Classifiers
- 构建联合隐变量模型捕捉专家间相关性
- 在保持高准确率前提下降低专家查询次数
- 适用于医疗影像等需专家参与的场景
机器学习应用常需结合模型输出与人类专家意见进行预测。本文研究如何在最少的人工标注代价下，利用预训练分类器的概率估计，获取专家对类别标签的判断。提出一种通用贝叶斯框架，通过联合隐表示建模专家相关性，支持基于模拟的推理，可评估额外专家查询的价值，并推断未观测专家标签的后验分布。在两个真实医学分类任务及CIFAR-10H、ImageNet-16H数据集上验证，相比基线方法显著降低专家查询成本，同时保持高预测准确率。
原文摘要 · Abstract (English)
Applications of machine learning often involve making predictions based on both model outputs and the opinions of human experts. In this context, we investigate the problem of querying experts for class label predictions, using as few human queries as possible, and leveraging the class probability estimates of pre-trained classifiers. We develop a general Bayesian framework for this problem, modeling expert correlation via a joint latent representation, enabling simulation-based inference about the utility of additional expert queries, as well as inference of posterior distributions over unobserved expert labels. We apply our approach to two real-world medical classification problems, as well as to CIFAR-10H and ImageNet-16H, demonstrating substantial reductions relative to baselines in the cost of querying human experts while maintaining high prediction accuracy.
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