切斯特射电报的提取和学习精精品标签 (Extracting and Learning Fine-Grained Labels from Chest Radiographs)

from arxiv, This paper won the Homer R. Warner Award at AMIA 2020 awarded to a paper that best describes approaches to improving computerized information acquisition, knowledge data acquisition and management, and experimental results documenting the value of these approaches. The paper shows a combination of textual and visual processing to automatically recognize complex findings in chest X-rays

Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray datasets to develop deep learning models for recognition of a handful of coarse finding classes such as opacities, masses and nodules. In this paper, we focus on extracting and learning fine-grained labels for chest X-ray images. Specifically we develop a new method of extracting fine-grained labels from radiology reports by combining vocabulary-driven concept extraction with phrasal grouping in dependency parse trees for association of modifiers with findings. A total of 457 fine-grained labels depicting the largest spectrum of findings to date were selected and sufficiently large datasets acquired to train a new deep learning model designed for fine-grained classification. We show results that indicate a highly accurate label extraction process and a reliable learning of fine-grained labels. The resulting network, to our knowledge, is the first to recognize fine-grained descriptions of findings in images covering over nine modifiers including laterality, location, severity, size and appearance.

翻译：最近,一些研究人员开始研究大型胸前X射线数据集,以开发深厚的学习模型,以识别少数粗糙的发现类,如孔径、质量和结核。在本论文中,我们的重点是提取和学习胸前X光图像的精细刻度标签。具体地说,我们开发了一种新的方法,从放射学报告中提取精细的标签,将词汇驱动的概念提取与依赖性剖析树中的细微组组合结合起来,以配合修饰者发现的结果。总共挑选了457个细细微的标签,描述迄今发现的最大范围,并获得了足够大的数据组,以培养为细度分类设计的新的深厚的学习模型。我们展示的结果显示,标签提取过程非常准确,并可靠地学习精细度标签。根据我们的知识,由此形成的网络首先认识到了对包括延度、地点、严重性、大小和外观在内的九个以上修饰者图像的精细度描述。