在临床专家参与的情况下自动化生成数据编程所需的弱标签
Automating Weak Label Generation for Data Programming with Clinicians in the Loop
AI总结：
针对医学领域高质量标注数据稀缺、高维场景下弱标注函数构建困难的问题，提出基于距离函数的算法，通过专家标注少量代表性样本生成弱标签，在医学时间序列和图像任务中均显著优于基线方法。
摘要：
大型深度神经网络（DNN）通常对数据需求量大，需要大量高质量的标注数据才能实现学习收敛。这在医学领域是一项挑战，因为高质量标注数据往往十分稀缺。数据编程在这方面带来了希望，因为它允许我们使用多个弱标注函数对未标注数据进行标注，这类函数通常由领域专家提供。数据编程可以将多个弱标注函数结合起来，生成的标注效果优于对不同函数结果进行简单多数投票的方式。然而，构建这类弱标注函数并非易事，尤其是在图像、时间序列数据等高维场景中。\n本文提出了一种利用距离函数规避这一问题的方法。在高维空间中，更容易找到可在不同标注任务间泛化的有意义的距离度量。我们提出了一种算法，可向专家查询数据集少量代表性样本的标签，这些样本由算法精心挑选，以捕捉数据集的分布特征。专家对代表性子集给出的标签可推导生成整个数据集的标注，从而生成可用于数据编程流程的弱标签。\n在我们的医学时间序列案例研究中，3265个样本中仅需标注50至130个子集样本，其准确率相比使用临床医生定义标注函数的基线提升了17%-28%，F1值提升了13%-28%。在我们的医学图像案例研究中，针对6293张未标注医学图像，使用我们的方法标注约50至120张图像子集，相比基线方法Snuba取得了显著提升，准确率约提升5%-15%，F1分数提升12%-19%。
英文摘要：
Large Deep Neural Networks (DNNs) are often data hungry and need high-quality labeled data in copious amounts for learning to converge. This is a challenge in the field of medicine since high quality labeled data is often scarce. Data programming has been the ray of hope in this regard, since it allows us to label unlabeled data using multiple weak labeling functions. Such functions are often supplied by a domain expert. Data-programming can combine multiple weak labeling functions and suggest labels better than simple majority voting over the different functions. However, it is not straightforward to express such weak labeling functions, especially in high-dimensional settings such as images and time-series data. What we propose in this paper is a way to bypass this issue, using distance functions. In high-dimensional spaces, it is easier to find meaningful distance metrics which can generalize across different labeling tasks. We propose an algorithm that queries an expert for labels of a few representative samples of the dataset. These samples are carefully chosen by the algorithm to capture the distribution of the dataset. The labels assigned by the expert on the representative subset induce a labeling on the full dataset, thereby generating weak labels to be used in the data programming pipeline. In our medical time series case study, labeling a subset of 50 to 130 out of 3,265 samples showed 17-28% improvement in accuracy and 13-28% improvement in F1 over the baseline using clinician-defined labeling functions. In our medical image case study, labeling a subset of about 50 to 120 images from 6,293 unlabeled medical images using our approach showed significant improvement over the baseline method, Snuba, with an increase of approximately 5-15% in accuracy and 12-19% in F1 score.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
