论文标题
肺炎：通过CXR图像检测肺炎时，CNN补偿了人类失真性
PneumoXttention: A CNN compensating for Human Fallibility when Detecting Pneumonia through CXR images with Attention
论文作者
论文摘要
通过机器的自动胸部X线X射线（CXR）解释是人工智能的重要研究主题。作为我在加利福尼亚科学博览会的旅程的一部分，我开发了一种算法，可以检测CXR图像中的肺炎以弥补人类的易失性。我的算法，肺炎，是两个13层卷积神经网络的合奏，该集合在RSNA数据集中训练，该数据集是由北美放射学会提供的数据集，其中包含26,684张额叶X射线图像，分为肺炎和无肺炎。该数据集由北美许多专业放射科医生注释。在测试集（RSNA数据集的20％随机拆分）上，它获得了令人印象深刻的F1分数，并在从RSNA和NIH绘制的25张测试图像上完全补偿了人类放射线医生。我没有直接比较，但斯坦福大学的Chexnet在NIH数据集的类别肺炎中的F1得分为0.435。
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.
