NIH Chest X-ray Dataset (Resized to 224x224)

This dataset is resized versions of images to 224x224. ![]() (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 neu

本数据集包含对图像进行缩放处理后的版本，尺寸为224x224。图像分类标签包括：1. 肺不张；2. 心脏肥大；3. 胸腔积液；4. 浸润；5. 肿块；6. 肉芽肿；7. 肺炎；8. 气胸；9. 炎症实变；10. 肿胀；11. 慢性阻塞性肺病；12. 纤维化；13. 胸膜增厚；14. 肋骨骨折。### 背景与动机：胸部X光检查是临床中最常见且经济有效的医学影像学检查之一。然而，胸部X光片的临床诊断具有一定的挑战性，有时甚至被认为比胸部CT成像的诊断难度更大。尽管在过去的报道中已有一些令人鼓舞的工作，特别是在近期关于结核病（TB）分类的深度学习研究中，但在现实世界的医疗场所实现针对胸部X光片所有数据设置的具有临床相关性的计算机辅助检测与诊断（CAD）仍然非常困难，甚至当仅使用数千张图像进行研究时，几乎是不可能实现的。这一点在文献[2]中得到了体现，其中深度神经网络的性能表现令人瞩目。

搜集汇总

背景与挑战

背景概述

NIH Chest X-ray Dataset是一个包含112,120张224x224尺寸胸部X光片的大型医学影像数据集，涵盖14种胸部疾病的多标签分类，适用于计算机辅助诊断研究。数据集来自美国国立卫生研究院临床中心，具有较高的临床代表性和研究价值。