NIH Chest X-ray Dataset of 14 Common Thorax Disease Categories

![]() (1, Atelectasis; 2, Cardiomegaly; 3, Effusion; 4, Infiltration; 5, Mass; 6, Nodule; 7, Pneumonia; 8, Pneumothorax; 9, Consolidation; 10, Edema; 11, Emphysema; 12, Fibrosis; 13, Pleural_Thickening; 14 Hernia) ### Background & Motivation: Chest X-ray exam is one of the most frequent and cost-effective medical imaging examination. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. Even some promising work have been reported in the past, and especially in recent deep learning work on Tuberculosis (TB) classification. To achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult, if not impossible when only several thousands of images are employed for study. This is evident from [2] where the performance deep neural networks for thorax disease recognition is severely

（1、肺不张；2、心增大；3、胸腔积液；4、浸润；5、肿块；6、结节；7、肺炎；8、气胸；9、实变；10、水肿；11、肺气肿；12、纤维化；13、胸膜增厚；14、疝气）
### 背景 与 动机：胸部X光检查是医学影像学中最常见且成本效益最高的检查之一。然而，胸部X光的临床诊断往往具有挑战性，有时甚至被认为比通过胸部CT成像的诊断更为困难。尽管过去已经有一些有希望的研究成果被报道，特别是在近期关于肺结核（TB）分类的深度学习研究中。要在现实世界的医疗场所实现与临床相关的计算机辅助检测和诊断（CAD），针对所有胸部X光数据设置仍然非常困难，甚至当仅使用数千张图像进行研究时，几乎不可能实现。这一点在[2]中得到了体现，其中深度神经网络在胸部疾病识别方面的性能受到了严重影响。