Fine-Grained Spatial Information Extraction For Radiology Reports

摘要 自动医学图像分类最近有了巨大的性能改进， 尤其是在放射学方面。然而，这些方法在阿尔茨海默病(AD)中的应用已经 由于相对较小的数据集和其对应的 表型。数据集大小问题是因为机器学习(ML)方法具有 要获得如此显著的性能，往往需要大量的标签数据来进行训练。 此外，表型粒度问题阻碍了对AD的有针对性的研究 在癌症等疾病的“精确医学”方法中看到了什么。然而，解决方案是存在的， 作为一种日益被接受的获取大量标记数据的方法，通过使用 如果是放射学报告，则对与图像相关联的自由文本报告进行自然语言处理(NLP 描述一个患者的AD相关发现，关联的图像(S)可以用来训练图像分类器。 该补充提案的父项目(R21EB029575)就提出了这样的NLP方法，而 通过提取细粒度的空间信息来同时解决粒度问题 报告情况。在父项目中，我们正在开发NLP资源和方法，以提高自动化 使用相应的研究报告标记放射学图像。父级不是特定于AD的(或 任何疾病)，所以这一补充将使我们能够专注于这种特别重要的疾病，它将 显著受益于改进的基于ML的成像。我们将专注于核磁共振和正电子发射计算机断层扫描。目标 这里与父项目并行，每个项目都侧重于专门改进AD的NLP的方法 放射学指标的提取以及来自对应的图像分类的验证 标签。 这些目标包括(1)扩展阿尔茨海默氏症的空间表示和语料库，(2)扩展 用于自动提取的NLP方法，以及(3)验证AD相关标签以用于图像 分类。 该项目的长期影响是通过扩大 基于ML的分类器可用的标记数据。短期目标补充是将我们的 NLP/Image联合研究提高AD诊断的复杂任务。通过扩展我们的 项目有一个特定的AD目标，我们将启动一个规模可观的研究工作，以实现这一目标。

ABSTRACT Automated medical image classification has seen enormous performance improvements recently, particularly in radiology. The application of these approaches to Alzheimer's Disease (AD), however, has been limited due to relatively small datasets and the limited granularity of their corresponding phenotypes. The dataset size issue is problematic as the machine learning (ML) methods that have achieved such remarkable performance often require enormous amounts of labeled data for training. Furthermore, the phenotype granularity issue impedes the targeted studying of AD along the lines of what is seen in the “precision medicine” approaches to diseases such as cancer. Solutions exist, however, as an increasingly accepted means of acquiring large amounts of labeled data is through the use of natural language processing (NLP) on the free-text reports associated with an image If a radiology report describes a patient's AD-related finding, the associated image(s) can be used to train an image classifier. The parent project to this supplemental proposal (R21EB029575) proposes just such a NLP method while simultaneously solving the granularity issue by extracting fine-grained spatial information from the report. In the parent project, we are developing NLP resources and methods to improve the automated labeling of radiology images using the corresponding study reports. The parent is not specific to AD (or any disease), so this supplement will enable us to focus on this particularly important disease, which will benefit significantly from improved ML-based imaging. We will focus on MRI and PET scans. The Aims here parallel the parent project, each focusing on methods that specifically improve NLP for AD radiological indicator extraction as well as the validation of image classification from the corresponding labels. These Aims include (1) extending the spatial representation and corpus for Alzheimer's, (2) extending the NLP methods for automatic extraction, and (3) validating the AD-related labels for use in image classification. The long-term impact of this project is to substantially improve AD diagnosis by scaling up the amount of labeled data available to ML-based classifiers. The short-term goal supplement is to focus our NLP/Imaging combination research on the complex task of improving AD diagnosis. By extending our project with a specific target for AD, we will initiate a sizable research effort toward this goal.