PneumoXttention：一种CNN，用于补偿通过带注意力的CXR图像检测肺炎时的人为错误
PneumoXttention: A CNN compensating for Human Fallibility when Detecting Pneumonia through CXR images with Attention
摘要：
X线胸片的机器自动判读是人工智能领域的一个重要研究课题。作为加州科学博览会之旅的一部分，我开发了一种算法，可以从CXR图像中检测肺炎，以弥补人类的不可靠性。我的算法PneumoXtention是在RSNA数据集上训练的两个13层卷积神经网络的集合，RSNA数据集是由北美放射学会提供的数据集，包含26，684张分为肺炎和非肺炎类别的正面X射线图像。该数据集由北美的许多专业放射科医生注释。它在测试集（RSNA数据集的20%随机分割）上取得了令人印象深刻的F1分数0.82，并在从RSNA和NIH抽取的25张测试图像的随机集上完全补偿了人类放射科医生。我没有直接的比较，但斯坦福大学的Chexnet在NIH数据集上的肺炎类别F1得分为0.435。
英文摘要：
Automatic Chest Radiograph X-ray (CXR) interpretation by machines is an important research topic of Artificial Intelligence. As part of my journey through the California Science Fair, I have developed an algorithm that can detect pneumonia from a CXR image to compensate for human fallibility. My algorithm, PneumoXttention, is an ensemble of two 13 layer convolutional neural network trained on the RSNA dataset, a dataset provided by the Radiological Society of North America, containing 26,684 frontal X-ray images split into the categories of pneumonia and no pneumonia. The dataset was annotated by many professional radiologists in North America. It achieved an impressive F1 score, 0.82, on the test set (20% random split of RSNA dataset) and completely compensated Human Radiologists on a random set of 25 test images drawn from RSNA and NIH. I don't have a direct comparison but Stanford's Chexnet has a F1 score of 0.435 on the NIH dataset for category Pneumonia.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
