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

A common problem in computer vision -- particularly in medical applications
-- is a lack of sufficiently diverse, large sets of training data. These
datasets often suffer from severe class imbalance. As a result, networks often
overfit and are unable to generalize to novel examples. Generative Adversarial
Networks (GANs) offer a novel method of synthetic data augmentation. In this
work, we evaluate the use of GAN- based data augmentation to artificially
expand the CheXpert dataset of chest radiographs. We compare performance to
traditional augmentation and find that GAN-based augmentation leads to higher
downstream performance for underrepresented classes. Furthermore, we see that
this result is pronounced in low data regimens. This suggests that GAN-based
augmentation a promising area of research to improve network performance when
data collection is prohibitively expensive.

本文探讨了在医疗领域电脑视觉技术中普遍存在的数据样本数量不足和分类失衡的问题，并通过使用生成对抗网络技术来增强数据集以改善网络性能，结果表明这种方法适用于低数据的情况，是值得进一步研究的领域。

基于 GAN 的胸部 X 光分类数据增强

GAN-based Data Augmentation for Chest X-ray Classification

The chest X-rays (CXRs) is one of the views most commonly ordered by
radiologists (NHS),which is critical for diagnosis of many different thoracic
diseases. Accurately detecting thepresence of multiple diseases from CXRs is
still a challenging task. We present a multi-labelclassification framework
based on deep convolutional neural networks (CNNs) for diagnos-ing the presence
of 14 common thoracic diseases and observations. Specifically, we trained
astrong set of CNNs that exploit dependencies among abnormality labels and used
the labelsmoothing regularization (LSR) for a better handling of uncertain
samples. Our deep net-works were trained on over 200,000 CXRs of the recently
released CheXpert dataset (Irvinandal., 2019) and the final model, which was an
ensemble of the best performing networks,achieved a mean area under the curve
(AUC) of 0.940 in predicting 5 selected pathologiesfrom the validation set. To
the best of our knowledge, this is the highest AUC score yetreported to date.
More importantly, the proposed method was also evaluated on an inde-pendent
test set of the CheXpert competition, containing 500 CXR studies annotated by
apanel of 5 experienced radiologists. The reported performance was on average
better than2.6 out of 3 other individual radiologists with a mean AUC of 0.930,
which had led to thecurrent state-of-the-art performance on the CheXpert test
set.

利用深度卷积神经网络（CNNs）构建了一个多标签分类框架，能够准确检测 14 种常见胸部疾病和病灶的存在，通过对 UGCXR 数据集的训练，该模型取得了目前最高的 AUC 分数 0.940， 平均优于独立评审组中的 2.6 位放射科医生，表现优于目前 CheXpert 测试集上的其他分类器。