Fine-Grained Spatial Information Extraction For Radiology Reports

摘要 自动医学图像分类最近已经看到了巨大的性能改进， 特别是在放射学方面。然而，将这些方法应用于阿尔茨海默病（AD）， 由于相对较小的数据集及其相应的粒度有限， 表型数据集大小问题是有问题的，因为机器学习（ML）方法具有 要达到如此出色的性能，通常需要大量的标记数据进行训练。 此外，表型粒度问题阻碍了沿着以下路线对AD进行靶向研究： 在治疗癌症等疾病的“精准医疗”方法中所看到的。然而，解决方案是存在的， 因为获取大量标记数据的越来越被接受的手段是通过使用 对与图像相关的自由文本报告进行自然语言处理（NLP） 描述了患者的AD相关发现，相关图像可用于训练图像分类器。 本补充提案（R21 EB 029575）的母项目提出了这样一种NLP方法， 同时通过从图像中提取细粒度的空间信息来解决粒度问题， 次报告.在母项目中，我们正在开发NLP资源和方法，以提高自动化的 使用相应的研究报告标记放射学图像。父代不是AD特有的（或 任何疾病），所以这个补充将使我们能够专注于这个特别重要的疾病，这将 从改进的ML成像中获益显著。我们将专注于MRI和PET扫描。目标 这里与父项目并行，每个项目都专注于专门改进AD的NLP的方法 放射学指标提取以及从相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征和语料库，（2）扩展 用于自动提取的NLP方法，以及（3）验证用于图像的AD相关标签 分类. 该项目的长期影响是通过扩大AD诊断的数量， 标记的数据可用于基于ML的分类器。短期目标补充是集中我们的 NLP/成像结合研究改善AD诊断的复杂任务。通过扩展我们 如果我们有一个针对AD的特定目标的项目，我们将朝着这一目标展开相当大的研究工作。

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.