In this paper, we introduce DRR-RATE, a large-scale synthetic chest X-ray
dataset derived from the recently released CT-RATE dataset. DRR-RATE comprises
of 50,188 frontal Digitally Reconstructed Radiographs (DRRs) from 21,304 unique
patients. Each image is paired with a corresponding radiology text report and
binary labels for 18 pathology classes. Given the controllable nature of DRR
generation, it facilitates the inclusion of lateral view images and images from
any desired viewing position. This opens up avenues for research into new and
novel multimodal applications involving paired CT, X-ray images from various
views, text, and binary labels. We demonstrate the applicability of DRR-RATE
alongside existing large-scale chest X-ray resources, notably the CheXpert
dataset and CheXnet model. Experiments demonstrate that CheXnet, when trained
and tested on the DRR-RATE dataset, achieves sufficient to high AUC scores for
the six common pathologies cited in common literature: Atelectasis,
Cardiomegaly, Consolidation, Lung Lesion, Lung Opacity, and Pleural Effusion.
Additionally, CheXnet trained on the CheXpert dataset can accurately identify
several pathologies, even when operating out of distribution. This confirms
that the generated DRR images effectively capture the essential pathology
features from CT images. The dataset and labels are publicly accessible at
this https URL

该研究介绍了 DRR-RATE，这是一个大规模的合成胸部 X 射线数据集，其中包括来自 21304 名独特患者的 50188 个前方数字重建放射图片（DRRs）。该数据集有助于开展包括 CT、X 射线影像、文本和二进制标签在内的新型多模态应用研究，并证明了 DRR-RATE 数据集在病理学特征捕捉方面的有效性。

阴影与光：用于疾病分类的数字重建放射图

Shadow and Light: Digitally Reconstructed Radiographs for Disease  Classification

We present a labeled large-scale, high resolution chest x-ray dataset for the
automated exploration of medical images along with their associated reports.
This dataset includes more than 160,000 images obtained from 67,000 patients
that were interpreted and reported by radiologists at Hospital San Juan
Hospital (Spain) from 2009 to 2017, covering six different position views and
additional information on image acquisition and patient demography. The reports
were labeled with 174 different radiographic findings, 19 differential
diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and
mapped onto standard Unified Medical Language System (UMLS) terminology. Of
these reports, 27% were manually annotated by trained physicians and the
remaining set was labeled using a supervised method based on a recurrent neural
network with attention mechanisms. The labels generated were then validated in
an independent test set achieving a 0.93 Micro-F1 score. To the best of our
knowledge, this is one of the largest public chest x-ray database suitable for
training supervised models concerning radiographs, and the first to contain
radiographic reports in Spanish. The PadChest dataset can be downloaded from
this http URL

本文介绍了一个标记大规模、高分辨率的胸部 X 光数据集，用于自动探索医学图像和相关报告，并在报告中标记了 174 个不同的放射学发现，19 个不同的差异诊断和 104 个不同的解剖部位位置，这些标记数据可用于训练监督模型关于放射学图像的分析与识别。此数据集可在 http URL 下载。